1RM Estimation

One-Repetition Maximum Estimation from Submaximal Loads

one-rep max prediction
estimated 1RM
strength prediction
maximal strength assessment

One-repetition maximum (1RM) estimation is a method to predict an athlete's maximum strength in a given lift without performing an actual maximal single repetition. Developed systematically by Matt Brzycki (1993) and refined by numerous researchers, 1RM estimation uses submaximal loads and repetition performance to extrapolate a strength ceiling. Rather than exposing untrained individuals, older adults, or post-injury athletes to the stress and injury risk of true 1RM testing, estimation provides a safer, time-efficient alternative. Multiple prediction equations exist, with varying accuracy depending on population and lift type.

Brzycki, M. (1993). Strength testing: predicting a one-rep max from reps-to-fatigue. Journal of Physical Education, Recreation and Dance, 64(1), 88-90.

Reynolds, J. M., Gordon, T. J., & Robergs, R. A. (2006). Prediction of one repetition maximum strength from multiple repetition maximum testing and anthropometry. Journal of Strength and Conditioning Research, 20(3), 584-592.

Mayhew, J. L., Johnson, B. D., LaMonte, M. J., Laubach, L. L., & Karg, K. (2008). Accuracy of prediction equations for determining 1-RM strength in apparently healthy adults. Journal of Sports Medicine and Physical Fitness, 48(4), 418-424.

force-velocity-profile rate-of-force-development reactive-strength-index isokinetic-dynamometry counter-movement-jump


A-star Search Algorithm

The A* Search Algorithm, developed by Peter E. Hart, Nils J. Nilsson, and Bertram Raphael in 1968, is an optimal path-finding algorithm that combines the benefits of Dijkstra's algorithm with heuristic guidance. It efficiently finds the shortest path by balancing actual distance from the start with estimated distance to the goal.

Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107.

Russell, S. J., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson.


AB Design

baseline-intervention design
AB single-case design
AB phase design

The AB design is the simplest single-subject experimental design, consisting of two sequential phases: a baseline phase (A) in which the target behavior is observed under natural conditions without intervention, followed by an intervention phase (B) in which the treatment or manipulation is introduced. Changes in the behavior's level, trend, or variability between phases are used to infer the effect of the intervention on the individual participant.

Repeatedly measured behavioral or outcome data over time

Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books.

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91-97.

aba-design abab-design multiple-baseline-design single-subject-experimental-design changing-criterion-design alternating-treatments-design


A/B Test

split test
controlled experiment
two-variant test
A/B Testi (Online Kontrollü Deney)

Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)

An A/B test is a randomized controlled experiment that simultaneously exposes two groups of users to a control variant (A) and a treatment variant (B) in order to determine whether a measured outcome differs significantly between them. The modern online controlled experiment framework was systematized by Ron Kohavi and colleagues at Microsoft in the early 2000s, building on R. A. Fisher's classical randomization principles from 1935. It is the dominant causal inference tool in web product development, digital marketing, and experimentation platforms.

Parametric comparison (frequentist or Bayesian)

frequentist z-test / t-test, Bayesian A/B, SPRT (Sequential Probability Ratio Test)

Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.

Deng, A., Xu, Y., Kohavi, R., & Walker, T. (2013). Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data. KDD '13.

independent-t-test chi-square-test proportions-z-test bayesian-ab-test multiarm-bandit randomized-controlled-trial


ABA Design

reversal design
withdrawal design
ABA withdrawal design

Montrose Wolf, Donald Baer, Todd Risley (applied behavior analysis tradition)

The ABA design is a single-subject experimental design that demonstrates experimental control through three sequential phases: a baseline phase (A1), an intervention phase (B), and a return-to-baseline withdrawal phase (A2). By removing the intervention in the final phase and observing whether behavior reverts toward baseline levels, researchers establish a functional relationship between the treatment and the target behavior for an individual participant.

Continuous behavioral observation data (frequency, rate, duration, intensity)

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.

Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.

ab-design abab-design multiple-baseline-design single-subject-experimental-design alternating-treatments-design changing-criterion-design


ABAB design

reversal design
withdrawal design
ABAB reversal
operant reversal design

Murray Sidman; Baer, Wolf & Risley (applied behavior analysis formalization)

The ABAB design is a single-subject experimental methodology that establishes causal control by repeatedly introducing and removing an intervention. A baseline phase (A) is followed by an intervention phase (B), then a return to baseline (A), and a second intervention phase (B), allowing the researcher to demonstrate that observed behavior changes are produced by the intervention rather than by coincidental factors.

Repeated measures of a behavioral or performance outcome over time

ab-design aba-design multiple-baseline-design single-subject-experimental-design alternating-treatments-design changing-criterion-design


Abbreviated Mental Test Score

The Abbreviated Mental Test (AMT) is a brief, 10-item cognitive screening instrument developed by Hodkinson in 1972 and originally published in Age and Ageing. It was specifically designed to quickly assess cognitive function in older hospitalized patients, detecting delirium and dementia in acute hospital settings. The AMT is valued for its simplicity, brevity (2–3 minutes), and utility in fast-paced clinical environments where quick cognitive triage is essential.

Brief clinician-administered cognitive screening instrument

Hodkinson, H. M. (1972). Evaluation of a mental test score for assessment of mental impairment in the elderly. Age and Ageing, 1(4), 233-238.

Swain, D. G., Nightingale, P. G., Constable, S. H., & Nightingale, J. M. (2007). Value of the Abbreviated Mental Test in screening for dementia and delirium among older people in the acute hospital setting. International Journal of Geriatric Psychiatry, 17(1), 63-69.

Bellelli, G., Nobili, A., Annoni, G., et al. (2014). Under-reporting of cognitive impairment in older hospitalized patients: The role of cognitive reserve. Journal of the American Geriatrics Society, 56(12), 2271-2276.

mmse saint-louis-mental-status dementia-rating-scale addenbrookes-cognitive-examination frontal-assessment-battery


Abbreviation Expansion

acronym resolution
abbreviation disambiguation
short-form expansion
Kısaltma ve Akronim Çözümleme

Schwartz & Hearst (2003) — seminal algorithm for biomedical abbreviation detection

Abbreviation and acronym resolution is a natural-language-processing pipeline that maps each short form in a text to its full-length definition using contextual cues from the surrounding text. It is especially important in medical, legal, and technical documents, where the same acronym may carry entirely different meanings across domains. The field's foundational algorithm was published by Schwartz and Hearst (2003) for biomedical literature and has since been extended by neural and transformer-based approaches.

Text corpus (medical, legal, technical, or general domain)

Output: Short-form → long-form mapping per occurrence

Health (1.6), Natural Language (1.4), Social (1.3), Education (1.3), Business (1.3)

Schwartz, A.S. & Hearst, M.A. (2003). A Simple Algorithm for Identifying Abbreviation Definitions in Biomedical Text. Pacific Symposium on Biocomputing (PSB), 8, 451-462.

Veyseh, A.P.B. et al. (2022). MACRONYM: A Large-Scale Dataset for Macroeconomic Acronym Understanding. Findings of NAACL 2022.

named-entity-recognition word-sense-disambiguation text-normalization information-extraction biomedical-text-mining


ABC Analysis

Pareto Inventory Classification
80-20 Inventory Rule
ABC Classification
ABC Stok Analizi

Pareto principle; Silver, Pyke & Peterson

ABC Analysis is a demand-value segmentation technique that divides inventory items into three classes — A, B, and C — based on their annual usage value (unit cost multiplied by annual demand). Rooted in the Pareto principle and codified for inventory management by Silver, Pyke, and Peterson (1998), it guides managers to concentrate control resources on the small fraction of items that drive the vast majority of total inventory spend.

Input: Annual usage value per SKU

Output: A / B / C tier assignment per item

Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling (3rd ed.). Wiley.


ABCD Matrix

ray transfer matrix
ABCD method
system matrix

The ABCD matrix, or ray transfer matrix method, is a compact algebraic framework for analyzing optical systems. Introduced by Kogelnik and Li in 1966, it represents the linear transformation of ray position and angle (or Gaussian beam parameters) through optical elements. This method is foundational in laser physics, Gaussian optics, and optical design, enabling rapid calculation of resonator stability, beam propagation, and system performance.

Kogelnik, H., & Li, T. (1966). Laser beams and resonators. Applied Optics, 5(10), 1550-1567.

Siegman, A. E. (1986). Lasers. University Science Books.

Gerrard, A., & Burch, J. M. (1974). Introduction to Matrix Methods in Optics. John Wiley & Sons.


Absorptive Capacity Scale

Absorptive Capacity (ACAP) Measurement Scale

Absorptive Capacity (ACAP) refers to an organization's ability to acquire, assimilate, transform, and exploit external knowledge to enhance innovation and performance. Zahra and George (2002) reconceptualized absorptive capacity into four distinct but interrelated processes in their foundational Academy of Management Review article. This measurement scale captures organizational learning dynamics and knowledge-based competitive advantage, making it essential for assessing innovation capability and knowledge management effectiveness.

Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203.

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

Jansen, J. J. P., Van Den Bosch, F. A. J., & Volberda, H. W. (2005). Managing potential and realized absorptive capacity: How do organizational antecedents matter? Academy of Management Journal, 48(6), 999–1015.

dynamic-capabilities-scale knowledge-management-scale organizational-resilience-scale entrepreneurial-orientation-scale strategic-orientation-scale


Abstract Writing

Academic Abstract Composition and Structure

abstract
structured abstract
unstructured abstract

Scientific publishing community; formalized by ICMJE and indexing services (MEDLINE, Web of Science)

An abstract is a self-contained, concise summary of a research article that enables readers to quickly understand the study's purpose, methods, results, and conclusions without reading the full paper. Abstracts are the primary gateway to published literature: they appear in journal issues, bibliographic databases (MEDLINE, Web of Science, Scopus), and search engine results. Well-written abstracts increase citation rates and visibility; poorly written ones obscure important research. The ICMJE and major journals mandate abstracts for original research, with structured formats (Background, Methods, Results, Conclusions) becoming increasingly standard.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.

Greenhalgh, T. (1997). How to read a paper: The basics of evidence based medicine. British Medical Journal, 315(7112), 180–184.

imrad-structure scientific-writing-clarity statistical-reporting-standards journal-submission-process


Academic Burnout Scale

Schaufeli, Martínez-Martínez, Marqués Pinto, Salanova

The Academic Burnout Scale measures three dimensions of student burnout: emotional exhaustion, cynicism toward studies, and reduced academic efficacy. Developed by Schaufeli and colleagues in 2002, it adapts the Maslach Burnout Inventory framework to the academic context, providing researchers and educators with a validated tool to assess psychological distress in higher education settings.

Schaufeli, W. B., Martinez, I. M., Marqués Pinto, A., Salanova, M., & Bakker, A. B. (2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464-481.

Maslach, C., Jackson, S. E., & Leiter, M. P. (2001). Maslach Burnout Inventory – Student Survey (MBI-SS). Consulting Psychologists Press.

procrastination-assessment-scale test-anxiety-inventory academic-resilience-scale study-skills-assessment academic-help-seeking-scale


Academic Help-Seeking Scale

The Academic Help-Seeking Scale measures students' inclination to seek academic help, their preferred sources of assistance (instructors, peers, tutors), and barriers that inhibit help-seeking (fear of judgment, embarrassment, preference for independence). Developed by Karabenick and colleagues in the 1990s, the AHSS recognizes that seeking help when confused or struggling is not a sign of weakness but a critical academic skill that separates successful from struggling students. By identifying whether students avoid help due to shame, lack of awareness, or other barriers, this scale enables targeted interventions promoting adaptive help-seeking.

Karabenick, S. A., & Knapp, J. R. (2005). Help seeking in learning. In C. E. Spielberger (Ed.), Encyclopedia of applied psychology (Vol. 2, pp. 149–160). Academic Press.

Arbreton, A. J. A. (1998). Student goal orientation and help-seeking strategy use. In S. A. Karabenick (Ed.), Strategic help seeking: Implications for learning and teaching (pp. 95–120). Lawrence Erlbaum.

academic-resilience-scale study-skills-assessment test-anxiety-inventory peer-learning-scale procrastination-assessment-scale


Academic Integrity Scale

Various authors; instrument varies by version

The Academic Integrity Scale measures students' attitudes, values, and likelihood of engaging in academic dishonesty including cheating, plagiarism, and unauthorized collaboration. Multiple validated versions exist, each assessing different facets of academic integrity such as personal integrity commitment, perceived cheating prevalence, institutional support for honesty, and personal susceptibility to cheating. This instrument is essential for understanding integrity culture in educational settings and evaluating interventions promoting academic honesty.

Cizek, G. J. (2003). Detecting and preventing classroom cheating: Practical strategies for teachers and professors. Corwin Press.

Steneck, N. H. (2007). Introduction to the Responsible Conduct of Research. US Department of Health and Human Services.

academic-burnout-scale procrastination-assessment-scale university-student-satisfaction study-skills-assessment academic-help-seeking-scale


Academic Motivation Scale

The Academic Motivation Scale (AMS) is a 28-item self-report instrument developed by Vallerand et al. (1992) to assess the quality of students' academic motivation. It distinguishes between intrinsic motivation (motivation for knowledge, accomplishment, and stimulation), extrinsic motivation (external regulation, introjected regulation, and identified regulation), and amotivation, grounded in self-determination theory.

Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003-1017.

Vallerand, R. J., Koestner, R., & Pelletier, L. G. (2000). Intrinsic and extrinsic motivation in sport: Examining their dynamic relationship. Journal of Applied Social Psychology, 30(5), 994-1017.

student-engagement-scale academic-self-efficacy-scale study-process-questionnaire school-climate-scale


Academic Resilience Scale

The Academic Resilience Scale measures the capacity of students to withstand and recover from academic adversity, including setbacks, failures, and difficult transitions. Developed by Cassidy in 2016, the ARS-30 conceptualizes resilience as a dynamic, multidimensional process involving perseverance, adaptive help-seeking, and emotional regulation—not a fixed trait. This instrument is invaluable for identifying students at risk of academic disengagement, evaluating resilience-building interventions, and understanding how students adapt to challenge.

Cassidy, S. (2016). The Academic Resilience Scale (ARS-30): A new multidimensional scale for measuring student resilience as a dynamic process. Frontiers in Psychology, 7, 1781.

Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.

academic-burnout-scale test-anxiety-inventory academic-help-seeking-scale procrastination-assessment-scale study-skills-assessment


Academic Self-Efficacy Scale

ASES
Self-Efficacy for Academic Performance

The Academic Self-Efficacy Scale (ASES) measures students' beliefs about their capability to succeed in academic tasks. Grounded in Bandura's social cognitive theory, the instrument assesses perceived competence in diverse academic domains—understanding lectures, completing assignments, performing on exams, and engaging in scholarly work. High academic self-efficacy is a strong predictor of achievement, persistence, and resilience in the face of academic challenges.

Bandura, A. (1977). Self-Efficacy: Toward a Unifying Theory of Behavioral Change. Psychological Review, 84(2), 191-215.

Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91.

academic-motivation-scale student-engagement-scale critical-thinking-dispositions-scale study-process-questionnaire


Accelerated Failure Time Model

Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)

Wei, L. J. (seminal review 1992); origins in parametric survival literature

The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how covariates shift the hazard rate, AFT models express the covariate effect as an acceleration or deceleration of the time axis itself.

Right-censoring; continuous and categorical covariates

Weibull, log-normal, log-logistic (selectable)

Wei, L. J. (1992). The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11(14–15), 1871–1879.

Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.

Kleinbaum, D. G. & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.

cox-ph kaplan-meier weibull-regression log-rank-test fine-gray-model


Accelerated Shelf-Life Testing

Accelerated Shelf-Life Testing (ASLT) uses elevated temperature and controlled storage conditions to rapidly assess product degradation and predict realistic shelf-life without waiting months. By measuring quality parameters (moisture, acidity, nutrient levels, microbial growth) at accelerated conditions and applying kinetic modeling, ASLT predicts expiration dates and optimal storage parameters before market launch.

Mizrahi, S. (1996). Kinetic models of food quality and shelf-life: A review. Journal of Food Quality, 19(4), 315-340.

Ahmad, U. K., & Ahmad, S. (2016). Application of kinetics and optics for food shelf-life testing. In Food quality and shelf life (pp. 234-267). Woodhead Publishing.


Acceptance and Commitment Therapy

Acceptance and Commitment Therapy (ACT) is a values-based, process-oriented psychotherapy developed by Steven C. Hayes and colleagues that helps individuals create meaningful lives while living with difficult thoughts, feelings, and sensations. Using mindfulness, values clarification, and behavioral commitment, ACT represents a third-wave approach to cognitive-behavioral therapy and has demonstrated effectiveness across diverse psychological problems.

Hayes, S. C., Luoma, J. B., Bond, F. W., Masuda, A., & Lillis, J. (2006). Acceptance and commitment therapy: Model, processes, and outcomes. Behaviour Research and Therapy, 44(1), 1–25.

Hayes, S. C., Strosahl, K. D., & Wilson, K. G. (2012). Acceptance and commitment therapy: The process and practice of mindful change (2nd ed.). Guilford Press.


Acculturation Rating Scale

Imelda Cuéllar, Bill Arnold, and Roberto Maldonado

The Acculturation Rating Scale for Mexican Americans (ARSMA) is a self-report measure designed to assess the degree to which Mexican American and Mexican immigrant individuals adopt or maintain cultural practices, values, and identity. Originally developed by Cuéllar, Harris, and Jasso in 1980 and revised as ARSMA-II in 1995, it measures bi-dimensional acculturation—the extent of both Mexican and American cultural orientation. The scale has been adapted for use with other immigrant and ethnic minority groups.

Cuéllar, I., Arnold, B., & Maldonado, R. (1995). Acculturation Rating Scale for Mexican Americans-II: A revision of the original ARSMA Scale. Journal of Cross-Cultural Psychology, 26(3), 307–319.

cultural-values-scale collectivism-individualism-scale modern-racism-scale environmental-attitudes-scale


Societal Attitudinal Familial Ethnic Acculturative Stress Scale (SAFE Scale)

The Societal Attitudinal Familial Ethnic (SAFE) Acculturative Stress Scale is a self-report instrument designed to measure the psychological stress and strain experienced by individuals during the acculturation process—the adaptation of cultural attitudes, behaviors, and identities when navigating between heritage and dominant cultures. Developed by Chavez, Cervantes, and Busch-Rossnagel in 1997, the SAFE Scale assesses stress across multiple domains: pressure to acculturate from society, family discord related to cultural differences, and experiences of discrimination. The instrument is widely used in clinical, educational, and research settings to evaluate acculturative stress among immigrant and ethnic minority populations and to understand its effects on mental health and well-being.

Chavez, R. A., Cervantes, R. C., & Busch-Rossnagel, N. A. (1997). Assessing acculturation in Mexican American adolescents. Hispanic Journal of Behavioral Sciences, 19(1), 80–93.

ethnic-identity-scale racism-and-life-experiences-scale social-distance-scale transcultural-self-efficacy-tool


Accuracy

Overall Accuracy
Correct Classification Rate

Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.

Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.

precision recall f1-score balanced-accuracy confusion-matrix


Adverse Childhood Experiences Questionnaire

Adverse Childhood Experiences (ACE) Questionnaire

Childhood adversity and trauma history assessment

The ACE Questionnaire is a 10-item instrument assessing exposure to adverse experiences during childhood, including abuse, neglect, and household dysfunction. Originally developed by Felitti and colleagues at Kaiser Permanente in 1998 as part of the landmark Adverse Childhood Experiences Study, the ACE Score quantifies cumulative childhood trauma and has become a foundational public health tool for identifying individuals at elevated risk for chronic physical and mental health conditions.

Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., & Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245-258.

Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C. H., Perry, B. D., & Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology. European Archives of Psychiatry and Clinical Neuroscience, 256(3), 174-186.


Achenbach Youth Self-Report

The Youth Self-Report (YSR), developed by Thomas Achenbach and Leslie Rescorla, is a youth-completed behavioral rating form assessing emotional and behavioral problems in adolescents aged 11–18 years. Part of the Achenbach System of Empirically Based Assessment (ASEBA), the YSR parallels the parent-completed Child Behavior Checklist (CBCL) and teacher-completed Teacher Report Form (TRF), enabling comprehensive multi-informant assessment of adolescent mental health and functioning.

Youth self-report behavioral rating form

Achenbach, T. M. (2003). Manual for the Youth Self-Report and 2003 Profile. University of Vermont Center for Children, Youth & Families.

Achenbach, T. M., & Rescorla, L. A. (2009). Manual for the ASEBA School-Age Forms & Profiles. University of Vermont Center for Children, Youth & Families.

cbcl-child-behavior conners-rating-scales strengths-difficulties-questionnaire vanderbilt-adhd-scale


ACL Return to Sport after Injury Scale

Kevin E. Webster, Julian A. Feller, Christopher Lambros

The ACL-Return to Sport after Injury (ACL-RSI) Scale is a 12-item patient-reported outcome instrument designed to measure the psychological impact and readiness to return to sport following anterior cruciate ligament injury and reconstruction. Developed by Webster, Feller, and Lambros in 2008 and published in the British Journal of Sports Medicine, the ACL-RSI addresses a critical gap in ACL rehabilitation assessment by quantifying psychological barriers to sport resumption—emotions, confidence in the knee, and risk appraisal—which are often more limiting than physical recovery.

Webster KE, Feller JA, Lambros C. Development and preliminary validation of a scale to measure the psychological impact of returning to sport after anterior cruciate ligament reconstruction surgery. Br J Sports Med. 2008;42(6):893-900.

ikdc-subjective-knee-form patient-specific-functional-scale global-rating-of-change-scale acl-return-to-sport-scale


Acne-QoL

Acne-QoL is a disease-specific, patient-administered quality-of-life measure assessing the psychological and social burden of acne vulgaris. Acne is the most common skin disease in adolescents and young adults and causes substantial psychological distress, depression, anxiety, and social impairment disproportionate to its severity. Multiple versions of Acne-QoL exist (19–24 items); all capture emotional, social, and functional impacts. Acne-QoL is essential in clinical trials and observational studies to ensure treatment efficacy encompasses quality-of-life outcomes.

Halvorsen JA, Stern RS, Dalgard F, et al. Suicidal ideation, mental health problems, and social impairment are increased in adolescents with acne: a population-based study. J Invest Dermatol. 2011;131(2):363-370.

Dréno B, Layton A, Zouboulis CC, López-Estebaranz JL, et al. Adult female acne: a practical approach. J Eur Acad Dermatol Venereol. 2013;27(Suppl 1):1-16.

poem skindex-29 dermatology-life-quality-index-children melasqol


Acoustic Design Analysis

Acoustic Design Analysis and Sound Performance Prediction

sound analysis
room acoustic design
noise prediction

Acoustic Design Analysis is a method for evaluating the acoustical properties of buildings to predict sound levels, reverberation time, and speech intelligibility. Founded by Wallace Clement Sabine in the early 1900s, the field encompasses room acoustic design (controlling reverberation), sound transmission loss (preventing noise transfer between spaces), and environmental noise prediction.

room acoustic prediction and assessment method

Sabine, W. C. (1922). Collected Papers on Acoustics. Harvard University Press, Cambridge, MA.

Kuttruff, H. (2009). Room Acoustics. Taylor and Francis, London, 5th edition.

Hopkins, C. (2007). Sound Insulation. Butterworth-Heinemann, Oxford.


Acoustic Doppler Current Profiler

The Acoustic Doppler Current Profiler (ADCP) is an instrument that uses Doppler-shifted acoustic backscatter to measure water velocity profiles along a vertical profile. Developed by RD Instruments in the 1980s, it has become the standard method for high-resolution current profiling in oceanographic research. ADCPs provide unprecedented spatial and temporal resolution of ocean circulation patterns.

RD Instruments. (1996). Acoustic Doppler Current Profiler Principles of Operation. A Practical Primer. RD Instruments Technical Note.

Teledyne Technologies. (2018). ADCP Technology and Applications. White Paper.


Acoustic Holography

Near-Field Acoustic Holography for Sound Field Reconstruction

NAH
near-field acoustics
sound field mapping
acoustic imaging

James Maynard, Earl Williams, Yongjian Lee

Near-Field Acoustic Holography (NAH) is a technique for reconstructing 3D acoustic sound fields and visualizing sound radiation from sources by measuring pressure at a dense microphone array in the near field. Pioneered by Maynard, Williams, and Lee in 1985, NAH extends holographic principles from optics to acoustics, enabling detailed acoustic source characterization, noise source identification, and acoustic field visualization that is impossible with conventional single-point or line-array methods.

Maynard, J. D., Williams, E. G., & Lee, Y. (1985). Near-field acoustic holography: I. Theory of generalized holography and the development of NAH. Journal of the Acoustical Society of America, 78(4), 1395–1413.

Williams, E. G. (1999). Fourier Acoustics: Sound Radiation and Nearfield Acoustical Holography. Academic Press.

Mueller, T. F. (2002). Aeroacoustic Measurements. Springer-Verlag.

room-impulse-response beamforming acoustic-ray-tracing bem-acoustics impedance-tube


Acoustic Phonetics

Acoustic Analysis of Speech
Spectrographic Analysis

Acoustic Phonetics is the study of the physical properties of speech sounds using instrumentation to measure and analyze sound waves. Pioneered by Peter Ladefoged and Kenneth Stevens, this method uses spectrograms, formant analysis, and waveform measurements to characterize vowels, consonants, and prosodic features with precision. Acoustic phonetics bridges the articulatory world of speech production and the perceptual world of listeners, providing objective, quantifiable data about how speech is produced and perceived.

Ladefoged, P., & Johnson, K. (2006). A Course in Phonetics (5th ed.). Boston: Cengage Learning.

Stevens, K. N. (2000). Acoustic Phonetics. Cambridge, MA: MIT Press.

Gordon, M. (2004). Phonetic structures of Turkish. Journal of the International Phonetic Association, 34(1), 34-52.


Acoustic Ray Tracing

Acoustic Ray Tracing for Room Simulation

ray tracing
geometric acoustics
image source method
sound ray propagation

Acoustic ray tracing is a computational technique for predicting sound propagation in rooms by treating acoustic energy as rays that reflect specularly off surfaces. Formalized by Allen and Berkley in 1979 via the image source method, ray tracing is one of the most computationally efficient methods for room acoustic simulation, especially for early and mid-reflections. It is widely used in audio engineering, architectural acoustics, and interactive spatial audio for virtual environments.

Allen, J. B., & Berkley, D. A. (1979). Image method for efficiently simulating small-room acoustics. Journal of the Acoustical Society of America, 65(4), 943–950.

Vorlaender, M. (1989). Simulation of room acoustics using the reciprocity theorem and ray tracing. Journal of the Acoustical Society of America, 86(1), 172–178.

Kuttruff, H. (2009). Room Acoustics (5th ed.). Spon Press.

room-impulse-response rt60-reverberation-time bem-acoustics acoustic-holography speech-intelligibility


Acoustic Telemetry

Acoustic Telemetry for Animal Movement Tracking

acoustic tracking
telemetry monitoring
underwater tracking

Acoustic telemetry is a remote tracking method in which small electronic transmitters attached to or implanted in animals emit unique acoustic signals detectable by underwater or terrestrial receiver networks, enabling real-time monitoring of animal movements, positions, and behavior over extended distances and times. Pioneered in fisheries research in the 1960s, acoustic telemetry is now standard for studying movement ecology, migration timing, and habitat use in aquatic and increasingly terrestrial systems.

Eiler, J. H. (2013). Acoustic telemetry. In C. R. Cooke & D. W. Philipp (Eds.), Telemetry Techniques and Technology (pp. 1-45). Springer.

Jepsen, N., Schreck, C., Clements, S., & Thorstad, E. B. (2005). Fish telemetry: tools and techniques. In D. B. Carlson & C. J. Bronte (Eds.), Fish Telemetry (pp. 3-28). American Fisheries Society.

Thorstad, E. B., Rikardsen, A. H., Alp, A., & Davidsen, J. G. (2013). The use of fish and other animal acoustic telemetry in European rivers: current status and future prospects. In A. R. Rikardsen & B. B. Dempson (Eds.), Atlantic Salmon Ecology (pp. 235-258). Wiley-Blackwell.


AcroQoL

Acromegaly Quality of Life Questionnaire

Growth hormone-excess-related quality of life

Markus Buchfelder, Dieter Weigel, Monica Droste, et al.

AcroQoL is a disease-specific 22-item quality of life questionnaire developed to assess the multidimensional burden of acromegaly, a chronic growth hormone-secreting pituitary tumor disorder. Developed by Buchfelder and colleagues in 2006, it captures physical complications (joint/bone pain, carpal tunnel syndrome, metabolic disease), appearance-related concerns (facial changes, hand/foot enlargement), and psychological/social impacts (mood, social limitation, body image). It is the primary outcome measure for assessing quality of life in acromegaly treatment trials and clinical practice.

Buchfelder, M., Weigel, D., Droste, M., et al. (2006). The quality of life of acromegaly patients is markedly impaired: Data from the German Acromegaly Registry. Eur J Endocrinol, 150(4), 541-549.

Ceraulo, D., Lombardi, G., & Colao, A. (2023). Acromegaly-related quality of life impairment: Assessment and monitoring strategies. Endocr Rev, 44(1), 94-116.


Action Research Arm Test

The Action Research Arm Test (ARAT) is a 19-item performance-based assessment measuring upper limb function in four domains: grasp, grip, pinch, and gross movement. Developed by Roberta Lyle in 1989, the ARAT has become the standard functional assessment for upper limb recovery in stroke rehabilitation, providing detailed measurement of hand and arm coordination relevant to activities of daily living.

Lyle, R. C. (1989). A performance test for assessment of upper limb function in physical rehabilitation treatment and research. International Journal of Rehabilitation Research, 12(6), 605-613.

Hsieh, C. L., Hsueh, I. P., Chiang, S. L., & Lin, C. H. (2007). Inter-rater reliability and validity of the action research arm test in stroke patients. Journal of Rehabilitation Medicine, 39(8), 654-660.


Action Research

Participatory Action Research
PAR
Collaborative Inquiry

Kurt Lewin; expanded by Kemmis, McTaggart, Reason & Bradbury

Action research is a collaborative research methodology in which researchers work with practitioners and community members to investigate a problem, implement change, and evaluate outcomes, cycling through reflection, action, and learning. Developed by Kurt Lewin (1946), action research bridges research and practice, aiming simultaneously to produce knowledge and practical improvement.

Lewin, K. (1946). Action research and minority problems. Journal of Social Issues, 2(4), 34–46.

Kemmis, S., & McTaggart, R. (2005). Participatory action research: Communicative action and the public sphere. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 559–603). Sage Publications.

Reason, P., & Bradbury, H. (Eds.). (2006). Handbook of action research: Participative inquiry and practice. Sage Publications.

participatory-action-research collaborative-inquiry reflective-practice implementation-science quality-improvement


Activated Sludge Model

Activated Sludge Process Simulation and Design

ASM
conventional activated sludge
suspended growth treatment

The Activated Sludge Model (ASM) is a standardized mathematical framework for simulating biological wastewater treatment processes, developed by the International Association on Water Quality (IAWQ) beginning in 1987. It represents the transport, transformation, and fate of organic matter and nutrients in suspended-growth treatment systems. ASM is widely used to design, optimize, and predict the performance of wastewater treatment plants under varying influent and operational conditions.

Henze, M., Grady, C. P. L., Gujer, W., Marais, G. V. R., & Matsuo, T. (1987). Activated Sludge Model No. 1. IAWQ, Scientific and Technical Report No. 1.

Grady, C. P. L., Daigger, G. T., & Lim, H. C. (1999). Biological Wastewater Treatment (2nd ed.). Marcel Dekker.

Gujer, W., Henze, M., Mino, T., & Matsuo, T. (1999). Activated Sludge Model No. 3. Water Science and Technology, 39(1), 183-193.

wastewater-treatment-design constructed-wetland-design biogas-production-model stormwater-management groundwater-contamination-model


Neutron Activation Analysis

Neutron Activation Analysis and Elemental Quantification

NAA
activation analysis
trace element analysis

Analytical chemistry with nuclear methods

Neutron activation analysis (NAA) is an analytical technique for determining elemental composition by bombarding samples with neutrons to produce radioactive isotopes, invented by de Hevesy and Levi in 1936. By measuring decay gamma rays from irradiated samples, NAA quantifies trace and major elements with high sensitivity, specificity, and accuracy without requiring destructive dissolution or complex sample preparation.

Chadwick, J. (1932). Possible Existence of a Neutron. Nature, 129(3252), 312.

Knoll, G. F. (2010). Radiation Detection and Measurement (4th ed.). John Wiley & Sons.

nuclear-decay-analysis dosimetry-measurement monte-carlo-neutron-particle neutron-transport-calculation radiation-dose-assessment


Active Disturbance Rejection Control

Active Disturbance Rejection Control (ADRC) is a control method that estimates and cancels disturbances and model uncertainties in real-time using an extended state observer (ESO), treating them as additional 'disturbance states'. Developed by Han and popularized by Gao, ADRC achieves remarkable robustness without requiring precise plant models, making it practical for real-world systems with significant uncertainty and disturbances.

Han, J. (2009). From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 56(3), 900-906.

Gao, Z. (2006). Active disturbance rejection control: a paradigm shift in feedback control system design. Proceedings of the 2006 American Control Conference, 2652-2661.


Active learning Association rules

Active Learning for Association Rule Mining

interactive association rule mining
active rule mining
query-driven association rule discovery
user-guided association rules

Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.

Active learning association rules combines the iterative query-and-label loop of active learning with association rule mining, allowing a human expert to guide the discovery process interactively. Instead of exhaustively enumerating all rules above a fixed support-confidence threshold, the system selects the most informative rule candidates and asks the user to judge their interestingness, focusing the search on subjectively useful patterns.

Dzyuba, V., & van Leeuwen, M. (2017). Interactive Discovery of Interesting Association Rules by Subjective Interestingness. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Springer.

Boley, M., Lucchese, C., Paurat, D., & Gartner, T. (2013). Direct Local Pattern Sampling by Efficient Two-Step Random Procedures. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 582–590). ACM.

association-rules active-learning apriori-algorithm fp-growth semi-supervised-association-rules interactive-machine-learning


Active Learning-Guided Autoencoder Anomaly Detection

query-guided autoencoder anomaly detection

Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving strong anomaly detection with only a small labeling budget.

Active learning + unsupervised deep anomaly detection hybrid

Unlabeled tabular, image, or sequential data with a small oracle-labeled budget

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.

Zhu, Y., Lukasiewicz, T. (2020). DPLAN: Discourse-level Plan-based Text Generation. Proceedings of the 28th International Conference on Computational Linguistics, 3464–3474. (See also: Guo et al. (2018). Deep Active Learning for Anomaly Detection. Neurocomputing, 290, 135–143.)


Active learning Boosting

boosting-based active learning
query learning with boosting
active boosting
ensemble active learning

Active Learning Boosting combines the query-driven label acquisition of active learning with the weighted-ensemble logic of boosting algorithms such as AdaBoost. The model iteratively selects the most informative unlabeled examples to annotate — guided by the disagreement or uncertainty within the boosting ensemble — and retrains after each new label, achieving high accuracy with far fewer labeled examples than passive learning.

Abe, N. & Mamitsuka, H. (1998). Query Learning Strategies Using Boosting and Bagging. Proceedings of the 15th International Conference on Machine Learning (ICML 1998), pp. 1–9. Morgan Kaufmann.

Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.

active-learning-random-forest active-learning-support-vector-machine boosting semi-supervised-learning online-boosting ensemble-boosting


Active learning Decision tree

Active Learning with Decision Tree Classifier

AL-DT
active decision tree
query-based decision tree learning
uncertainty-sampling decision tree

Settles, B. (active learning framework); Breiman et al. (decision tree base)

Active learning with a decision tree combines the interpretable structure of a CART-style tree with a query strategy that selects the most informative unlabeled instances for human annotation. The model iteratively requests labels only for examples it is most uncertain about, minimising labeling cost while maximising classification accuracy on tabular data.

Active learning with decision tree base learner

Tabular, labeled and unlabeled instances

Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth & Brooks.

decision-tree active-learning semi-supervised-decision-tree random-forest active-learning-random-forest active-learning-logistic-regression


Active Learning Federated Learning

Federated Active Learning (Active Learning within Federated Learning)

Federated Active Learning
FAL
Active Federated Learning
distributed active learning

Multiple authors (federated active learning emerged ~2020)

Federated Active Learning combines the annotation-efficiency of active learning with the privacy-preserving decentralization of federated learning. A shared global model is trained across distributed clients, each of which independently ranks its unlabeled local data and requests labels only for the most informative examples, keeping raw data on-device throughout.

Hybrid paradigm (active querying within distributed training)

Distributed, decentralized labeled and unlabeled datasets across clients

Ro, J. Y., Ali, A., Lin, Z., & Suresh, A. T. (2021). Scaling Federated Learning for Fine-tuning of Large Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP).

federated-learning active-learning semi-supervised-learning online-learning transfer-learning self-supervised-learning


Active learning Gaussian mixture model

Active Learning with Gaussian Mixture Model

AL-GMM
active GMM
query-by-committee GMM
active density estimation

Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)

Active Learning Gaussian Mixture Model combines an iterative query strategy with a Gaussian Mixture Model learner. The algorithm selects the most informative unlabeled points — typically those with highest predictive uncertainty — presents them to an oracle for labeling, and refits the GMM using EM on the growing labeled set. The result is a density model that matches full-data quality while requiring far fewer labeled examples.

Active learning for probabilistic clustering / density estimation

Continuous, unlabeled or partially labeled tabular data

Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML), 912–919.

Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool Publishers.

gaussian-mixture-model semi-supervised-learning active-learning-k-means active-learning-gaussian-process semi-supervised-gaussian-mixture-model bayesian-gaussian-mixture-model


Active learning Gaussian process

Active Learning with Gaussian Process (GP-AL)

GP active learning
Gaussian process active learning
GP-AL
Bayesian active learning with GP

Active Learning Gaussian Process (GP-AL) combines a Gaussian process probabilistic model with an active learning query strategy, using the GP's posterior uncertainty to select the most informative unlabeled examples for labeling. This iterative approach minimizes labeling effort while maximizing predictive accuracy, making it ideal when labeled data is scarce or expensive to obtain.

Continuous, mixed; labeled and unlabeled tabular data

MacKay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Computation, 4(4), 590–604.

Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool.

gaussian-process active-learning bayesian-gaussian-process semi-supervised-gaussian-process gaussian-mixture-model k-nearest-neighbors


Active Learning Gradient Boosting

Active Learning with Gradient Boosting (Query-by-Committee / Uncertainty Sampling with Gradient Boosted Trees)

AL-GBM
gradient boosting active learner
active gradient boosting
active learning with boosted trees

Settles, B. (active learning); Friedman, J. H. (gradient boosting); combined framework developed by the research community

Active Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examples for human annotation. By querying only the instances the model is most uncertain about, the method achieves high accuracy with far fewer labeled examples than passive supervised learning.

Active learning framework with gradient boosting base learner

Labeled and unlabeled tabular or structured data

Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.

Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.

gradient-boosting xgboost random-forest active-learning query-by-committee uncertainty-sampling


Active learning Isolation forest

Active Learning with Isolation Forest for Anomaly Detection

active anomaly detection with isolation forest

Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)

Active Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most informative instances. The result is a detector that refines its anomaly boundaries using a minimal labeling budget, dramatically improving precision on rare and subtle anomalies compared to a purely unsupervised baseline.

Active learning wrapper over isolation forest anomaly detector

Continuous / mixed tabular data with unlabeled anomalies and limited oracle budget

Das, S., Wong, W. K., Fern, A., Dietterich, T. G., & Amran Siddiqui, M. (2019). Incorporating Expert Feedback into Active Anomaly Discovery. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), pp. 1009–1014.

Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.

isolation-forest active-learning one-class-svm autoencoder-anomaly-detection semi-supervised-isolation-forest gaussian-mixture-model


Active learning K-nearest neighbors

Active Learning with K-Nearest Neighbors Classifier

AL-KNN
active KNN
query-based nearest neighbor learning
uncertainty-sampling KNN

Settles, B. (active learning framework); Fix & Hodges (KNN base)

Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far fewer labeled examples than fully supervised KNN on tabular data.

Zhu, X., Lafferty, J., & Ghahramani, Z. (2003). Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data, 58–65.

k-nearest-neighbors active-learning active-learning-decision-tree active-learning-logistic-regression semi-supervised-k-nearest-neighbors active-learning-random-forest


Active Learning LightGBM

Active Learning with Light Gradient Boosting Machine

AL-LightGBM
Active LightGBM
LightGBM active learning
AL-LGBM

Settles, B. (active learning); Ke, G. et al. (LightGBM)

Active Learning LightGBM couples the query-efficient label-selection strategy of active learning with the speed and accuracy of LightGBM, a histogram-based gradient boosting framework. The model iteratively selects the most informative unlabeled instances for human annotation, retrains LightGBM on the growing labeled set, and converges to high accuracy with far fewer labeled examples than passive supervised learning.

Hybrid (active learning query strategy + gradient boosting classifier)

Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154.

lightgbm active-learning xgboost random-forest uncertainty-sampling gradient-boosting


Active Learning Linear Regression

optimal experimental design for regression

Cohn, D. A.; Ghahramani, Z.; Jordan, M. I.

Active Learning Linear Regression is an iterative machine-learning approach that couples a linear regression model with an intelligent query strategy to select the most informative unlabeled points for labeling. By focusing labeling effort where uncertainty is highest, it achieves competitive predictive accuracy with far fewer labeled examples than passive random sampling.

Active learning / iterative supervised learning

Continuous labels, partially labeled datasets

Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129–145.

linear-regression bayesian-linear-regression gaussian-process-regression random-forest uncertainty-sampling query-by-committee


Active Learning Logistic Regression

Active Learning with Logistic Regression (Uncertainty Sampling)

uncertainty sampling logistic regression

Lewis, D. D. & Gale, W. A.; Settles, B. (survey)

Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling.

Active learning framework with logistic regression base learner

Labeled and unlabeled tabular or text data

Lewis, D. D., & Gale, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 3–12.

logistic-regression support-vector-machine naive-bayes random-forest semi-supervised-learning query-by-committee


Active learning One-class SVM

Active Learning with One-Class Support Vector Machine

AL-OCSVM
active one-class SVM
active novelty detection SVM
query-driven OCSVM

Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)

Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves its decision boundary with minimal labeling effort.

Semi-supervised anomaly/novelty detection with iterative labeling

Continuous or mixed features; predominantly unlabeled with a small labeled set

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443–1471.

Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.

one-class-svm active-learning support-vector-machine isolation-forest semi-supervised-learning anomaly-detection


Active Learning Self-supervised Learning

Active Learning with Self-supervised Representation Learning

AL-SSL
active self-supervised learning
self-supervised active learning
query-based self-supervised learning

Multiple authors (active learning + SSL integration, 2020s)

Active learning combined with self-supervised learning leverages unlabeled data through self-supervised pre-training to build rich representations, then uses an active query strategy to select the most informative examples for human annotation, maximizing model performance under a tight labeling budget. This hybrid approach is especially powerful when labeled data is scarce but large unlabeled pools exist.

Largely unlabeled datasets with limited annotation budget

Bengar, J. Z., van de Weijer, J., Fuentes, L. L., & Raducanu, B. (2022). Class-Balanced Active Learning for Image Classification. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3082–3091.

Wang, K., Zhang, D., Li, Y., Zhang, R., & Lin, L. (2016). Cost-Effective Active Learning for Deep Image Classification. IEEE Transactions on Circuits and Systems for Video Technology, 27(12), 2591–2600.

self-supervised-learning active-learning semi-supervised-learning transfer-learning few-shot-learning online-learning


Active learning Stacking ensemble

AL-stacking
query-by-committee stacking
active stacked generalization
stacking with active query

Wolpert, D. H. (stacking); Settles, B. (active learning survey)

Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.

Hybrid (active learning + stacked ensemble)

Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.

Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers.

stacking-ensemble active-learning semi-supervised-stacking-ensemble ensemble-stacking-ensemble voting-ensemble boosting


Active learning Support vector machine

Active SVM
AL-SVM
SVM active learning
query-by-committee SVM

Active learning SVM combines the strong decision-boundary of support vector machines with an intelligent query strategy that selects the most informative unlabeled instances for human annotation. Introduced by Tong and Koller in 2001, it achieves high classification accuracy using far fewer labeled examples than passive supervised learning, making it practical whenever labeling is expensive or slow.

Labeled and unlabeled instances (tabular, text, image features)

Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2, 45–66.

svm-classification support-vector-machine query-by-committee semi-supervised-learning uncertainty-sampling random-forest


Active Learning Voting Ensemble

Active Learning with Voting Ensemble (Query by Committee)

Query by Committee
QBC
active ensemble learning
committee-based active learning

Active Learning Voting Ensemble — formally known as Query by Committee — is an active learning strategy that trains a committee of diverse models and selects the unlabeled examples where the committee members disagree most for human annotation. By focusing labeling effort on the most informative points, it achieves high accuracy with far fewer labeled examples than passive learning requires.

Labeled and unlabeled tabular, text, or image data

Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT '92), pp. 287–294. ACM.

active-learning voting-ensemble semi-supervised-learning boosting bagging query-by-disagreement


Active Learning

Optimal Experimental Design (ML context)

Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.

Interactive supervised learning framework

Uncertainty sampling, query-by-committee, expected model change

Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.


ABC Scale

Activities-Specific Balance Confidence Scale

The Activities-Specific Balance Confidence (ABC) Scale is a self-report questionnaire developed by Powell and Myers in 1995 to measure an older adult's confidence in maintaining balance while performing 16 common daily activities. Unlike performance-based balance tests, the ABC Scale captures self-efficacy—the person's subjective belief in their ability to perform activities without losing balance or falling. It is widely used in clinical practice, rehabilitation, and research to identify individuals at high fall risk due to low balance confidence and to measure outcomes of interventions designed to restore confidence and activity participation.

Powell, L. E., & Myers, A. M. (1995). The Activities-Specific Balance Confidence (ABC) Scale. J Gerontol A Biol Sci Med Sci, 50A(1), M28-M34.

Lajoie, Y., & Gallagher, S. P. (2004). Predicting falls within the elderly community: comparison of postural sway, reaction time, the Berg balance scale and the Activities-Specific Balance Confidence (ABC) in determining fall risk. Arch Phys Med Rehabil, 85(7), 1100-1105.

Huang, S. L., Hsieh, C. L., Wu, R. M., Tai, C. H., Lin, C. H., & Lu, W. S. (2011). Minimal detectable change of the timed up and go test and the dynamic gait index in people with Parkinson disease. Phys Ther, 91(1), 114-121.

tinetti-balance-assessment short-physical-performance-battery life-space-assessment frail-scale edmonton-frail-scale


Activity-Based Costing

Activity-Based Costing (ABC) Framework for Product and Service Cost Allocation

ABC System
Activity-Based Management
Activity Costing

Activity-Based Costing (ABC) is an advanced costing method developed by Robert Kaplan and Robin Cooper that allocates overhead and indirect costs to products or services based on their actual consumption of activities. Rather than using arbitrary allocation bases (e.g., machine hours or direct labor), ABC traces costs to specific activities (purchasing, machine setup, quality control) and then to products based on which products actually consume those activities, providing more accurate product costs for decision making.

Advanced managerial accounting methodology

Cooper, R., & Kaplan, R. S. (1991). Profit priorities from activity-based costing. Harvard Business Review, 69(3), 130-135.

Garrison, R. H., Noreen, E. W., & Brewer, P. C. (2015). Managerial accounting (15th ed.). McGraw-Hill Education.


Acute-Chronic Workload Ratio

Acute-Chronic Workload Ratio and Injury Risk Assessment

ACWR
workload ratio
training load balance

The acute-chronic workload ratio (ACWR) is the ratio of acute training load (typically the past 1 week) to chronic training load (typically the rolling 4-week average). Formalized by Tim Gabbett (2016), ACWR is a widely adopted metric for predicting injury and illness risk in sports. The logic is straightforward: rapid increases in training load—when acute load spikes far above what the athlete has adapted to—exceed tissue tolerance and increase injury risk. Conversely, maintaining ACWR within optimal ranges (typically 0.8-1.3) is associated with better performance and lower injury incidence. ACWR monitoring is now standard in elite sports for load management.

Gabbett, T. J. (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273-280.

Blanch, P., & Gabbett, T. J. (2016). Has the athlete trained enough to return to play safely? New concepts in return-to-play rehabilitation. British Journal of Sports Medicine, 50(13), 807-811.

Hulin, B. T., Gabbett, T. J., Blanch, P., Chapman, P., Bailey, D., & Orchard, J. W. (2014). Spikes in acute workload are associated with increased injury risk in elite Australian footballers. British Journal of Sports Medicine, 48(12), 997-1002.


AdaBoost

AdaBoost (Adaptive Boosting)
adaptive boosting
adaptif artırma

AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.

Ensemble (sequential boosting of weak learners)

Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.

xgboost random-forest decision-tree stacking-ensemble logistic-regression


Adaptive AB Design

Adaptive AB Single-Subject Experimental Design

adaptive single-case AB design
data-driven AB design
adaptive baseline-intervention design
adaptive AB phase design

1968 (AB foundation); 2000s (adaptive extensions)

Baer, Wolf & Risley (AB foundation); Kratochwill & Levin (adaptive single-case extensions)

The adaptive AB design is a single-subject experimental design that retains the two-phase baseline-then-intervention structure of the classic AB design but replaces fixed session-count rules with pre-specified data-driven criteria — such as stability thresholds or trend benchmarks — that determine when to transition between phases. This adaptive logic allows the phase boundary to move in response to the individual participant's actual performance trajectory rather than a predetermined schedule.

Single-subject experimental design with adaptive phase-change rules

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91-97.

Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.

ab-design aba-design abab-design adaptive-experiment multiple-baseline-design single-subject-experimental-design


Adaptive A/B test

adaptive AB test
bandit A/B test
multi-armed bandit testing
online adaptive experiment

1952 (Robbins); applied to A/B testing from ~2010s onward

Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson

An Adaptive A/B test is an experimental design that dynamically reallocates traffic or participants toward better-performing variants during the experiment itself, rather than holding allocations fixed until the end. Drawing on multi-armed bandit algorithms such as Thompson Sampling or Upper Confidence Bound (UCB), it balances the exploration of uncertain variants with the exploitation of those already showing superior performance, typically yielding higher aggregate outcomes while still producing valid inferential conclusions.

Continuous or binary outcome metrics (e.g., click-through, conversion rates)

Russo, D., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends in Machine Learning, 11(1), 1–96.

Offer-Westort, M., Coppock, A., & Green, D. P. (2021). Adaptive Experimental Design: Prospects and Applications in Political Science. American Journal of Political Science, 65(4), 826–844.

adaptive-experiment multi-arm-experiment ab-design factorial-ab-test randomized-controlled-trial blocked-ab-test


Adaptive ABA Design

Adaptive ABA Single-Subject Experimental Design

adaptive withdrawal design
adaptive ABA withdrawal design
data-driven ABA design
adaptive single-case ABA

1968 (ABA foundation); adaptive extensions formalized ~2010–2020

Baer, Wolf & Risley (ABA baseline); adaptive decision-rule extensions developed in single-case methodology literature (Kratochwill & Levin, 2010s)

The Adaptive ABA Design is a single-subject experimental framework that follows the classic three-phase ABA withdrawal structure — baseline (A1), intervention (B), and return-to-baseline (A2) — while embedding prospective decision rules that allow researchers or clinicians to extend, shorten, or otherwise modify each phase in response to observed data patterns rather than following a fixed schedule. This adaptive layer makes the design responsive to individual participant trajectories while preserving experimental control.

Single-subject experimental design with adaptive phase rules

Repeated-measures behavioral or clinical outcome data (continuous or count)

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.

aba-design abab-design adaptive-experiment single-subject-experimental-design multiple-baseline-design adaptive-ab-design


Adaptive ABAB Design

adaptive reversal design
adaptive single-subject ABAB
ABAB with adaptive phase-change rules
dynamic ABAB design

1984 (foundational ABAB); adaptive extensions ~2000s–2010s

Extended from Barlow & Hersen's ABAB reversal tradition; adaptive rules formalized in behavioral and clinical single-subject research (late 20th–early 21st century)

The Adaptive ABAB Design is a single-subject experimental methodology that extends the classic ABAB reversal design by incorporating data-driven, prospective decision rules to determine when to transition between baseline (A) and intervention (B) phases. Rather than fixing phase lengths in advance, the researcher uses pre-specified criteria — such as stability thresholds, slope targets, or effect-size benchmarks — to guide each phase change, improving both experimental control and clinical responsiveness.

Repeated measures (behavioral observation, continuous outcome data)

Barlow, D. H., & Hersen, M. (1984). Single Case Experimental Designs: Strategies for Studying Behavior Change (2nd ed.). Pergamon Press.

Normand, M. P., & Bailey, J. S. (2006). The human right to effective behavioral treatment. The Behavior Analyst, 29(2), 253–261.

abab-reversal-design multiple-baseline-design alternating-treatments-design changing-criterion-design single-subject-design interrupted-time-series


Adaptive Case-Control Study

adaptive case-control design
sequential case-control study
adaptive observational study
dynamic case-control study

1950s (base design); adaptive extensions developed from the 1970s–1990s

Builds on Doll & Hill (1950s) case-control foundations; adaptive elements drawn from sequential analysis (Wald, 1947) and group-sequential methods (Armitage, 1975)

An adaptive case-control study is a case-control design that incorporates pre-specified rules allowing modification of study parameters — such as sample size, case-to-control ratio, or matching criteria — based on interim data, without compromising validity. It combines the efficiency of adaptive methodology with the retrospective exposure-ascertainment logic of classical case-control research, enabling investigators to respond to emerging evidence while the study is ongoing.

Adaptive observational epidemiological design

Categorical and continuous exposure/outcome data; binary disease status

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

Jennison, C., & Turnbull, B. W. (1999). Group Sequential Methods with Applications to Clinical Trials. Chapman & Hall/CRC.

case-control-study adaptive-randomized-clinical-trial sequential-analysis nested-case-control cohort-study adaptive-cohort-study


Adaptive case series

adaptive case-series design
sequential adaptive case series
adaptive observational case series

Evolved from classical case series methodology combined with adaptive design principles (Chow & Chang, 2008; FDA adaptive design guidance)

An adaptive case series is an observational study design that documents a consecutive group of patients with a shared condition or exposure while incorporating pre-specified rules for modifying data collection, monitoring, or stopping criteria as accumulating evidence warrants. It combines the descriptive richness of traditional case series with the prospective flexibility of adaptive design principles, enabling structured mid-course adjustments without compromising the integrity of the recorded clinical observations.

Observational study with adaptive monitoring

Clinical records, patient follow-up data, safety and outcome reports

Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.

case-series adaptive-randomized-clinical-trial adaptive-cohort-study prospective-case-series adaptive-diagnostic-accuracy-study case-report


Adaptive Cluster Sampling

ACS
adaptive network sampling
sequential cluster sampling
neighborhood adaptive sampling

Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups.

Probability-based adaptive sampling design

Numeric counts or measurements of rare, clustered populations

Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.

Thompson, S. K., & Seber, G. A. F. (1996). Adaptive Sampling. Wiley.

cluster-sampling systematic-sampling stratified-sampling snowball-sampling multistage-sampling adaptive-stratified-sampling


Adaptive Cohort Study

adaptive longitudinal study
flexible cohort design
adaptive prospective cohort
ACS

Extension of classic cohort methods; adaptive design principles formalised by regulatory and epidemiology communities in the 2000s–2010s

An adaptive cohort study is a longitudinal observational design that follows a defined group of individuals over time to assess exposure-outcome relationships, while incorporating pre-specified adaptation rules that allow protocol modifications — such as sample-size re-estimation, subgroup enrichment, or measurement schedule adjustments — based on accumulating interim data. Adaptations are made without compromising validity, guided by a statistical analysis plan agreed upon before data collection begins.

Observational / adaptive epidemiological design

Longitudinal individual-level data; exposure, outcome, covariate measurements over time

VanderWeele, T. J., & Hernan, M. A. (2012). Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs. American Journal of Epidemiology, 175(12), 1303–1310.

cohort-study prospective-cohort-study adaptive-clinical-trial longitudinal-study case-control-study interrupted-time-series


Adaptive Competing Risks Analysis

adaptive subdistribution hazard analysis

1999 (foundational Fine-Gray model); adaptive extensions 2000s–2010s

Fine & Gray (subdistribution hazard, 1999); adaptive extensions by Beyersmann, Schumacher and colleagues

Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study to be modified mid-course (e.g., sample size reassessment, early stopping) based on accumulating competing-risk data while maintaining pre-specified type I error control.

Statistical survival analysis with adaptive interim monitoring

Time-to-event data with multiple mutually exclusive failure types; censored observations

Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.

Beyersmann, J., Allignol, A., & Schumacher, M. (2012). Competing Risks and Multistate Models with R. Springer.

competing-risks-analysis cause-specific-hazard-model fine-gray-model group-sequential-design adaptive-trial-design survival-analysis


Adaptive Control Group Experimental Design

Adaptive Experimental Design with Control Group

adaptive controlled experiment
adaptive two-arm controlled design
adaptive parallel-group design
flexible controlled trial design

1994 (formal adaptive framework); wider adoption 2000s–2010s

Peter Bauer and Klaus Kohne (adaptive interim analysis framework, 1994); broader adaptive design methodology developed by Scott Chow and Mark Chang

An adaptive control group experimental design is an experiment that assigns participants to at least one treatment arm and one concurrent control group, while allowing pre-specified modifications to the trial — such as sample size re-estimation, early stopping, or allocation ratio changes — based on accumulating data. Adaptations are governed by decision rules established before the study begins, preserving Type I error control while improving efficiency.

Continuous, binary, or time-to-event outcome data from treatment and control groups

Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC.

Bauer, P., & Kohne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics, 50(4), 1029–1041.

adaptive-experiment randomized-controlled-trial control-group-experimental-design adaptive-randomized-controlled-trial sequential-experimental-design response-adaptive-randomization


Adaptive Control

Self-Tuning Control
Parameter Estimation Control

Adaptive Control is a control strategy that adjusts controller parameters in real-time based on online system identification to maintain performance despite changing plant dynamics or uncertain parameters. Pioneered by Astrom and Wittenmark, adaptive control enables robust operation in time-varying environments, from aircraft with fuel depletion to industrial systems with aging components.

Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.

Ioannou, P. A., & Sun, J. (1996). Robust Adaptive Control. Prentice Hall.


Adaptive Cox Proportional Hazards

adaptive regularized survival regression

2007 (adaptive LASSO variant); base Cox model 1972

Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox

The Adaptive Cox Proportional Hazards model extends the classic Cox regression for time-to-event outcomes by adding adaptive LASSO (or related) penalization. It simultaneously estimates hazard ratios and performs variable selection, shrinking irrelevant covariate coefficients exactly to zero. This makes it especially valuable in high-dimensional clinical or genomic datasets where the number of candidate predictors is large relative to the number of events.

Penalized semi-parametric survival regression

Time-to-event (survival) data with right-censoring; continuous and/or binary predictors

Zhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703.

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.

cox-proportional-hazards lasso-regression elastic-net-survival kaplan-meier accelerated-failure-time random-survival-forest


Adaptive Cross-Sectional Epidemiological Study Design

adaptive cross-sectional survey
adaptive prevalence study
adaptive epidemiological survey design
adaptive population cross-section

1990s–2000s (formalization of adaptive elements in observational surveys)

Conceptual synthesis of adaptive design methods (Wald, 1947; Bauer & Kohne, 1994) with classical cross-sectional epidemiology (MacMahon & Pugh, 1960s)

An adaptive cross-sectional epidemiological study combines the core logic of a cross-sectional survey — measuring exposures and outcomes simultaneously in a defined population at one point in time — with pre-specified adaptive rules that allow modifications to sampling strategy, sample size, or subgroup allocation based on accumulating interim data. The approach preserves the efficiency and speed of a standard cross-sectional design while improving precision for rare exposures or heterogeneous populations by redirecting sampling resources in real time.

Observational epidemiological study design

Population-level survey data; prevalence counts; exposure and outcome measurements at a single or adaptively timed time point

Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.

cross-sectional-study adaptive-sampling two-phase-sampling stratified-random-sampling ecological-study repeated-cross-sectional-study


Adaptive Clinical Trial Design

Adaptif Klinik Çalışma Tasarımı (Adaptive Design)

Adaptive clinical trial design is a flexible experimental framework, formalised by Bauer and Köhne in 1994, in which pre-specified rules allow the trial to be modified mid-course — adjusting sample size, treatment arms, or randomisation ratios — based on accumulating interim data while rigorously controlling the Type I error rate.

Adaptive hypothesis test with interim analyses

Bauer, P. & Köhne, K. (1994). Evaluation of Experiments with Adaptive Interim Analyses. Biometrics, 50(4), 1029–1041.

FDA (2019). Adaptive Design Clinical Trials for Drugs and Biologics — Guidance for Industry. U.S. Food and Drug Administration.

sequential-design equivalence-trial randomized-controlled-trial group-sequential-test sample-size-calculation


Adaptive Diagnostic Accuracy Study

adaptive DTA study
adaptive diagnostic test evaluation
adaptive test accuracy trial
adaptive STARD study

2000s–2010s (adaptive designs codified for diagnostics ~2010s)

Adaptation of STARD framework (Bossuyt et al.) combined with adaptive design principles (Jennison & Turnbull; FDA guidance)

An adaptive diagnostic accuracy study evaluates how well an index test distinguishes between patients with and without a target condition, while incorporating pre-specified interim analyses that allow modifications — such as sample size re-estimation, threshold adjustment, or subgroup enrichment — based on accumulating data. This design improves efficiency and ethical conduct compared to fixed-sample diagnostic studies, particularly when prior prevalence or test performance data are uncertain.

Adaptive observational/experimental study design

Index test results, reference standard results, patient covariates

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., ... & Cohen, J. F. (2015). STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527.

Jennison, C., & Turnbull, B. W. (2000). Group Sequential Methods with Applications to Clinical Trials. Chapman & Hall/CRC.

diagnostic-accuracy-study adaptive-randomized-clinical-trial screening-test-evaluation adaptive-cohort-study bayesian-diagnostic-accuracy-study prospective-diagnostic-accuracy-study


Adaptive Dose-Response Analysis

adaptive DRA
adaptive dose-finding analysis
adaptive exposure-response analysis
adaptive D-R modeling

2000s (key papers 2005–2007; ICH E4 guidance 1994 for classical dose-response)

Frank Bretz, José Pinheiro and colleagues; foundational MCP-Mod framework

Adaptive dose-response analysis combines pre-specified dose-response modeling with planned interim looks that allow modifications — such as dropping ineffective doses or reallocating sample size — while maintaining statistical integrity. The most widely cited framework is MCP-Mod (Multiple Comparisons and Modeling), endorsed by the EMA and FDA as a fit-for-purpose methodology for dose-finding studies in drug development.

Adaptive statistical design and analysis

Continuous or binary outcome data with multiple pre-specified dose levels

Bretz, F., Pinheiro, J. C., & Branson, M. (2005). Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics, 61(3), 738-748.

Bornkamp, B., Bretz, F., Dmitrienko, A., Enas, G., Gaydos, B., Hsu, C. H., König, F., Mohberg, M., Pinheiro, J., Roessner, L., & Smith, M. (2007). Innovative approaches for designing and analyzing adaptive dose-ranging studies. Journal of Biopharmaceutical Statistics, 17(6), 965-995.

dose-response-analysis adaptive-randomized-clinical-trial adaptive-phase-ii-clinical-trial bayesian-dose-response-analysis adaptive-phase-i-clinical-trial screening-test-evaluation


Adaptive Ecological Study

adaptive ecologic study
sequential ecological study
adaptive population-level design
adaptive group-level study

1990s–2000s (adaptive extensions of classical ecological designs)

Building on classical ecological epidemiology (Durkheim, Snow, Morgenstern); adaptive extensions developed in late 20th–early 21st century methodological literature

An adaptive ecological study is an observational epidemiological design in which the unit of analysis is a group or population (e.g., a region, country, or community) rather than an individual. It extends the classical ecological study by incorporating pre-specified interim decision rules that allow modifications — such as changes in geographic unit, time window, or exposure categorisation — as data accumulate, while preserving overall inferential validity. The design is used to explore population-level associations between aggregate exposures and aggregate outcomes.

Aggregate population-level data (rates, proportions, means per group or region)

Morgenstern, H. (1998). Ecologic studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.

ecological-study cross-sectional-study interrupted-time-series adaptive-trial-design multilevel-analysis disease-mapping


Adaptive Experiment

adaptive design
response-adaptive randomization
adaptive trial
adaptive randomization

1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s

Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others

An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour.

Continuous, binary, or count outcome data; interim accumulating data

Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC.

U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.

multi-arm-experiment randomized-controlled-trial factorial-experiment sequential-analysis platform-trial response-surface-methodology


Adaptive Field Experiment

adaptive field trial
sequentially adaptive field experiment
responsive field experiment
adaptive randomized field study

1990s–2000s (formalized in field economics and development research contexts)

Developed at the intersection of adaptive trial methodology (Berry, Bauer) and field experimentation (Duflo, Kremer, List)

An adaptive field experiment is a randomized study conducted in a real-world environment in which pre-specified decision rules allow the researcher to modify the trial as interim data accumulate — for example, by reallocating participants toward more effective arms, adjusting sample size, or stopping early for efficacy or futility — all while maintaining statistical integrity.

Adaptive experimental design conducted in naturalistic settings

Continuous or interim outcome data collected in real-world field conditions

Berry, D. A. (2004). Bayesian statistics and the efficiency and ethics of clinical trials. Statistical Science, 19(1), 175–187.

Duflo, E., Glennerster, R., & Kremer, M. (2007). Using randomization in development economics research: A toolkit. Handbook of Development Economics, 4, 3895–3962.

adaptive-experiment field-experiment randomized-controlled-trial multi-arm-experiment adaptive-randomized-controlled-trial factorial-field-experiment


Adaptive Fractional Factorial Experiment

adaptive FFE
sequential fractional factorial design
adaptive screening design
adaptive factor screening

1950s–1960s (classical FFD); adaptive extensions formalized in 1990s–2000s

Box, Hunter, and collaborators (adaptive/sequential extension of classical fractional factorial work)

An adaptive fractional factorial experiment combines the resource-efficiency of fractional factorial designs with a sequential, data-driven strategy for selecting which factors and interactions to investigate next. Rather than committing all experimental runs upfront, the researcher analyses results from an initial fraction and uses those findings to guide subsequent rounds of experimentation — augmenting, folding, or redirecting the design until the active factors and optimal settings are identified with sufficient precision.

Continuous or categorical factor levels; quantitative response outcomes

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.

Wu, C. F. J., & Hamada, M. S. (2000). Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley.

fractional-factorial-design response-surface-methodology plackett-burman-design central-composite-design sequential-experimentation definitive-screening-design


Adaptive Full Factorial Experiment

Adaptive Full Factorial Experimental Design

adaptive full-factorial design
sequential full factorial experiment
adaptive complete factorial design
dynamic full factorial trial

1950s (factorial foundations); adaptive extensions prominent from 1990s onward

Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory

An adaptive full factorial experiment is an experimental design that starts with a complete crossing of all factors and all their levels, then uses interim data to modify subsequent runs — dropping unpromising factor levels, adding new ones, or re-allocating replication — while preserving the full factorial structure within each phase. This integration of full factorial coverage with adaptive decision rules allows researchers to explore all main effects and interactions without committing to a fixed, inefficient run plan before any data are observed.

Continuous or categorical outcomes measured across all factor-level combinations

Atkinson, A., Donev, A., & Tobias, R. (2007). Optimum Experimental Designs, with SAS. Oxford University Press.

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

full-factorial-experiment adaptive-experiment fractional-factorial-experiment response-surface-methodology sequential-experimental-design factorial-randomized-controlled-trial


Adaptive Laboratory Experiment

adaptive lab experiment
sequential adaptive laboratory study
response-adaptive laboratory design
adaptive experimental laboratory design

1947 (sequential analysis foundations); adaptive laboratory applications widespread from 1990s

Rooted in sequential analysis (Abraham Wald, 1947); adaptive clinical/lab designs formalized by Berry and colleagues (1990s–2000s)

An adaptive laboratory experiment is a controlled experimental design conducted in a laboratory setting where pre-specified decision rules allow modifications to the study — such as sample size, treatment allocation, or stopping criteria — based on accumulating data. Unlike fixed designs, adaptive designs incorporate planned interim analyses that permit the experiment to respond to emerging evidence while maintaining statistical validity and Type I error control.

Continuous or binary outcome data collected in controlled laboratory conditions

Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.

adaptive-experiment laboratory-experiment adaptive-randomized-controlled-trial multi-arm-experiment sequential-analysis response-adaptive-randomization


Adaptive LMS Filter

LMS Filter
Adaptive LMS Algorithm
Gradient Descent Filtering

The Least Mean Squares (LMS) filter is an adaptive signal processing algorithm that continuously updates filter coefficients to minimize the squared error between the filter output and a desired signal. Introduced by Bernard Widrow and Marcian Hoff in 1960, the LMS algorithm is one of the most widely used adaptive filtering techniques due to its simplicity, low computational cost, and ability to track time-varying signals.

Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE Wescon Convention Record, 4, 96–104.

Haykin, S. (2002). Adaptive Filter Theory (4th ed.). Prentice Hall.

wiener-filter kalman-filter-signal fir-filter-design iir-filter-design


Adaptive Maximum Variation Sampling

Adaptive Maximum Variation Purposive Sampling

adaptive purposive maximum variation sampling

1990s–2000s (practice codified in qualitative methods literature)

Synthesizes Patton (maximum variation) and Thompson (adaptive sampling)

Adaptive maximum variation sampling is a purposive qualitative sampling strategy that combines the logic of maximum variation sampling — deliberately selecting cases that differ as widely as possible on key dimensions — with an adaptive, iterative recruitment process. Rather than fixing the full sample in advance, the researcher continuously reviews emerging data to identify which types of cases are underrepresented and recruits new participants to fill those gaps, maximizing heterogeneity throughout data collection.

Adaptive purposive qualitative sampling strategy

Qualitative or mixed-methods data; interview, observational, or document data

Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. [Maximum variation sampling, pp. 169–183]

Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.

maximum-variation-sampling purposive-sampling adaptive-cluster-sampling theoretical-sampling snowball-sampling adaptive-stratified-sampling


Adaptive Multi-Arm Experiment

MAMS design
multi-arm adaptive trial
adaptive platform trial
response-adaptive multi-arm experiment

2000s–2010s (MAMS framework formalized c. 2003–2011)

Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others

An adaptive multi-arm experiment simultaneously evaluates several treatment conditions against a common control and modifies the trial in real time based on accumulating data — dropping ineffective arms early, reallocating participants toward promising ones, or adjusting sample sizes — all while controlling error rates. The approach maximizes information gained per participant and reduces the time and cost required to identify effective treatments relative to running sequential separate trials.

Continuous, binary, or time-to-event outcome data from multiple treatment arms

Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256.

Wason, J., Magirr, D., Law, M., & Jaki, T. (2016). Some recommendations for multi-arm multi-stage trials. Statistical Methods in Medical Research, 25(2), 716–727.

multi-arm-experiment adaptive-experiment factorial-randomized-controlled-trial platform-trial response-adaptive-randomization bayesian-adaptive-design


Adaptive Multiple Baseline Design

Adaptive Multiple Baseline Single-Case Experimental Design

adaptive MBD
flexible multiple baseline design
adaptive SCED multiple baseline
data-driven multiple baseline design

1968 (multiple baseline base); adaptive extensions discussed from ~2000s onward

Baer, Wolf & Risley (multiple baseline foundation); adaptive modifications developed within single-case methodology community

The Adaptive Multiple Baseline Design is a single-case experimental design that applies the standard multiple baseline logic — staggering intervention onset across two or more tiers (behaviors, settings, or participants) — but allows phase-change decisions to be guided by ongoing data review rather than fixed, pre-specified schedules. This flexibility makes the design more responsive to participant variability while preserving the core replication logic that supports causal inference.

Repeated measures of a single participant or small-n across behaviors, settings, or participants

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.

Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.

multiple-baseline-design ab-design aba-design abab-design adaptive-experiment single-subject-experimental-design


Adaptive Multistage Sampling

AMS
adaptive multi-phase sampling
sequential multistage sampling
adaptive hierarchical sampling

1977 (multistage base); 1990-1992 (adaptive extensions by Thompson)

Steven K. Thompson (adaptive principles); William G. Cochran (multistage framework)

Adaptive multistage sampling combines the hierarchical efficiency of multistage designs with adaptive decision rules that adjust which units are sampled at later stages based on what is observed at earlier stages. It is used when a target characteristic is rare, clustered, or spatially heterogeneous and a fixed design would waste resources on uninformative areas of the population.

Quantitative; population counts, measurements, or proportions across hierarchical units

Thompson, S. K. (1992). Sampling. Wiley.

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.

multistage-sampling adaptive-cluster-sampling adaptive-stratified-sampling systematic-sampling cluster-sampling sequential-sampling