FAI

activities of daily living, instrumental ADL

The Frenchay Activities Index (FAI) is a self-report or informant-rated questionnaire designed to measure participation in activities of daily living and instrumental activities over a 3-month period. Developed by Holbrook and Skilbeck (1983) at the Frenchay Hospital in Bristol, the FAI evaluates participation in 15 activities spanning domestic, leisure, and work domains. The FAI is widely used in stroke rehabilitation and aging research to measure broader functional recovery, social participation, and return to valued activities beyond basic self-care.

Holbrook, M., & Skilbeck, C. E. (1983). An activities index for use with stroke patients. Age and Ageing, 12(2), 166-170.

Schuling, J., de Haan, R., Limburg, M., & Groenier, K. H. (1993). The Frenchay Activities Index. Assessment of functional status in stroke patients. Stroke, 24(8), 1173-1177.

copm upper-extremity-functional-scale occupational-self-assessment frenchay-activities-index


Text Frequency Analysis

Text Frequency Analysis (Word and N-gram Frequency Analysis)

word frequency analysis
n-gram frequency analysis
Metin Frekans Analizi

George K. Zipf (frequency-distribution foundation)

Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.

Output: Frequency table / ranked term counts

Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.

Manning, C. D. & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.

lexical-diversity tf-idf topic-modeling sentiment-analysis


Frequency analysis

frequency distribution
frequency table
tally analysis
count analysis

Classical statistics (no single inventor)

Frequency analysis is a fundamental descriptive technique that tallies how often each distinct value or category appears in a dataset. It produces absolute counts, relative percentages, and cumulative frequencies, giving an immediate picture of how observations are distributed across categories. It is the natural first step when exploring categorical or discrete variables before applying inferential tests.

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.

Agresti, A. (2007). An Introduction to Categorical Data Analysis (2nd ed.). Wiley.

chi-square-goodness-of-fit cross-tabulation-analysis descriptive-statistics bar-chart pie-chart binomial-test


FreTS

FreTS (Frequency-domain MLPs for Forecasting)

Frequency-domain MLPs
FrequencyMLP
FreTS Forecaster
Frekans Alanı MLP

FreTS is a time series forecasting architecture introduced by Yi et al. at NeurIPS 2023. It departs from Transformer-based designs by applying simple Multi-Layer Perceptrons (MLPs) entirely in the frequency domain. The model transforms input sequences with the Discrete Fourier Transform and then learns temporal and channel dependencies through complex-valued MLP layers, achieving competitive or superior long-term forecasting accuracy with substantially lower computational cost.

Yi, K., Zhang, Q., Fan, W., Wang, S., Wang, P., He, H., An, N., Lian, D., Cao, L., & Niu, Z. (2023). Frequency-domain MLPs are more effective learners in time series forecasting. NeurIPS.


Friedman test

Friedman two-way analysis of variance by ranks

The Friedman test is a nonparametric hypothesis test that compares three or more related conditions measured on the same blocks or subjects, serving as the rank-based alternative to repeated-measures ANOVA. It was introduced by Milton Friedman in 1937 and works on ordinal or continuous data without assuming normality.

Nonparametric repeated-measures comparison (by ranks)

ordinal or continuous, measured repeatedly within blocks

k - 1, where k is the number of conditions

Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.

repeated-measures-anova kruskal-wallis-test wilcoxon-signed-rank-test permutation-test


Friendship Quality Questionnaire

FQQ
Friendship Quality Index
Friendship Assessment Scale

friendship quality and peer relationships

Jeffrey Parker and Steven Asher (developmental version); expanded by Bukowski and others

The Friendship Quality Questionnaire is a self-report instrument designed to assess the quality and characteristics of friendships in children, adolescents, and adults. Developed by Jeffrey Parker and Steven Asher in 1993 and expanded by Bukowski and colleagues, the FQQ measures dimensions of friendship quality including companionship (spending time together), conflict, help and guidance, intimate disclosure, closeness, loyalty, and conflict resolution. The FQQ is widely used in developmental psychology, peer relationship research, and clinical assessment of social functioning across the lifespan.

Parker, J. G., & Asher, S. R. (1993). Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction. Developmental Psychology, 29(4), 611-621.

Bukowski, W. M., Hoza, B., & Boivin, M. (1994). Measuring friendship quality during adolescence: The approaches and perspectives. Journal of Social and Personal Relationships, 11(3), 471-484.


Frontal Assessment Battery

executive function and frontal lobe assessment

The Frontal Assessment Battery (FAB) is a brief, clinician-administered neuropsychological battery designed to assess frontal lobe function and executive abilities at the bedside. Developed by Dubois and colleagues at the Salpêtrière Hospital in Paris in 2000, the FAB consists of six subtests measuring conceptualization, mental flexibility, motor planning, inhibitory control, and verbal fluency. The FAB is particularly sensitive to frontotemporal dementia, Parkinson's disease with cognitive decline, and other conditions affecting prefrontal function.

Clinician-administered neuropsychological battery for frontal lobe function

Dubois, B., Slachevsky, A., Litvan, I., & Pillon, B. (2000). The FAB: A Frontal Assessment Battery at bedside. Neurology, 55(11), 1621-1626.

Slachevsky, A., Litvan, I., Marsiske, M., & Dubois, B. (2004). The FAB: A Frontal Assessment Battery at bedside for dementia and neurological conditions. In Dementia (pp. 123-146). Springer Publishing.

Cummings, J. L., & Mega, M. (1994). Neuropsychiatry and behavioral neuroscience. Oxford University Press.

trail-making-test mmse adas-cog addenbrookes-cognitive-examination dementia-rating-scale


Frontdoor Adjustment

Frontdoor Adjustment (Frontdoor Criterion)

Ön Kapı Düzenlemesi (Frontdoor Adjustment)

Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured.

Causal identification (graphical adjustment)

Two-stage regression over a full mediator

Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.

Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.

backdoor-adjustment iv-2sls causal-discovery dag-identification regression-discontinuity


Fruit Color Analysis

Spectrophotometric Fruit Color Assessment

color grading
chromatic analysis
colorimetry
ripeness grading

Commission Internationale de l'Eclairage (CIE)

Fruit color analysis employs spectrophotometric measurement to quantify ripeness and quality based on chromatic properties. Using the CIE L*a*b* color space, introduced in 1976, this non-destructive method objectively grades fruit maturity and predicts sensory acceptability. It is widely applied in commercial sorting lines and research settings for precision quality control.

McGuire, R. G. (1992). Reporting objective color measurements. HortScience, 27(12), 1254–1255.

Peirs, A., Tirry, N., Verlinden, B., & Nicolaï, B. M. (2004). Sampling and optical detection in automated high-throughput apple sorting. Journal of Agricultural Engineering, 35(1), 18–27.

brix-measurement ripeness-index postharvest-storage-simulation cold-storage-protocol


Fuzzy-Set Qualitative Comparative Analysis (fsQCA) is a set-theoretic method developed by Charles Ragin in the early 2000s that combines the configurational logic of qualitative case studies with the mathematical rigor of fuzzy sets. It bridges qualitative and quantitative research by allowing researchers to examine causal complexity through combinations of conditions (configurations) rather than isolated variables.

Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press.

Ragin, C. C. (2006). Set relations in social research: Evaluating their consistency and coverage. Political Analysis, 14(3), 291-310.

Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420.

necessity-condition-analysis pls-sem rule-space-methodology process-tracing multiple-factor-analysis


FT-ICR Mass Spectrometry

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

FT-ICR-MS
Fourier Transform ICR
ICR mass spectrometry

Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry is an advanced analytical technique that combines magnetic confinement of ions with Fourier transform data processing to achieve exceptional mass accuracy and resolution. Developed by Comisarow and Marshall in 1974, FT-ICR-MS enables the determination of exact masses and elemental compositions of complex molecules, making it invaluable for environmental chemistry, metabolomics, petroleum characterization, and structural elucidation of unknowns.

Comisarow, M. B., & Marshall, A. G. (1974). Fourier transform ion cyclotron resonance spectroscopy. Chemical Physics Letters, 25(2), 282-283.

Marshall, A. G., Hendrickson, C. L., & Jackson, G. S. (1998). Fourier transform ion cyclotron resonance mass spectrometry: A primer. Mass Spectrometry Reviews, 17(1), 1-35.

Shi, S. D., Drader, J. J., Freitas, M. A., Hendrickson, C. L., & Marshall, A. G. (2000). Comparison of proteins in human plasma using accurate mass and proteolytic digestion with ion cyclotron resonance mass spectrometry. Journal of Proteome Research, 5(11), 3289-3298.

maldi-tof atr-ftir ft-icr-mass-spectrometry electron-paramagnetic-resonance


FUCA

FUCA (Flexible and Universal Compromise Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Raveh, A. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weighted rank aggregation (Borda-type with rank-based weights)

Raveh, A. (2000). Co-plot: A graphic display method for geometrical representations of MCDM. European Journal of Operational Research

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


FUCOM-F

FUCOM-F (Fuzzy Full Consistency Method (TFN)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Pamucar, D., Ecer, F. in 2018 crisp; 2020 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Triangular-fuzzy pairwise priority weighting with full-consistency NLP

Pamucar, D., Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method – FUCOM-F. Facta Universitatis, Series: Mechanical Engineering

topsis vikor edas marcos mabac waspas copras aras


FUCOM

FUCOM (Full Consistency Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Stević, Ž., Sremac, S. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise priority + consistency constraint LP weighting

Pamučar, D., Stević, Ž., Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM models: Full consistency method (FUCOM). Symmetry

ahpsort aploco aras aroman artasi cobra cocoso codas


Fugl-Meyer Assessment

Fugl-Meyer Assessment of Sensorimotor Recovery

The Fugl-Meyer Assessment (FMA) is a comprehensive, clinician-administered scale measuring sensorimotor recovery and motor impairment in stroke patients. Developed by Fugl-Meyer and colleagues in 1975, FMA has become the gold standard outcome measure in stroke rehabilitation research and clinical practice for quantifying motor recovery in the upper extremity, lower extremity, balance, and sensation.

Fugl-Meyer, A. R., Jääskö, L., Leyman, I., Olsson, S., & Steglind, S. (1975). The post-stroke hemiplegic patient: a method for evaluation of physical performance. Scandinavian Journal of Rehabilitation Medicine, 7(2), 13–31.

Gladstone, D. J., Danells, C. J., & Black, S. E. (2002). The Fugl-Meyer Assessment of Motor Recovery after Stroke: a critical review of its measurement properties. Neurorehabilitation and Neural Repair, 16(3), 232–240.

mini-best-test tug-test berg-balance-scale nihss


Full Factorial Experiment

full factorial design
complete factorial design
2^k factorial design
FFD

1926 (Fisher's foundational paper); codified by the 1950s–1960s

A full factorial experiment runs every possible combination of all chosen factor levels, making it the gold standard for simultaneously estimating main effects, two-way interactions, and higher-order interactions among multiple independent variables. Introduced through Ronald Fisher's foundational work on factorial designs in the 1920s and systematised by Box, Hunter, and Montgomery, it provides complete information about how factors act individually and in combination on an outcome.

Continuous or categorical outcome measurements under controlled conditions

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

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

fractional-factorial-experiment factorial-experiment randomized-controlled-trial response-surface-methodology latin-square-design blocked-full-factorial-experiment


Fully Convolutional Network (FCN)

Fully Convolutional Network for Semantic Segmentation

The Fully Convolutional Network (FCN), introduced by Long, Shelhamer, and Darrell at CVPR 2015, was the first end-to-end deep learning architecture trained to produce dense pixel-wise semantic segmentation maps from images of arbitrary size. By replacing the fully connected layers of a classification CNN with convolutional layers and adding learned upsampling through transposed convolutions and skip connections, FCN enabled the direct prediction of a class label for every pixel in an image, establishing the template for all subsequent segmentation architectures including U-Net and DeepLab.

Dense pixel-wise prediction convolutional network

Arbitrary spatial size (any height × width)

32 (FCN-32s), 16 (FCN-16s), or 8 (FCN-8s) pixels per prediction

Adapted from VGG-16 / AlexNet / GoogLeNet classifiers

Long, J., Shelhamer, E., & Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.

Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640–651.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9). MIT Press.

u-net deeplab segnet convolutional-neural-network encoder-decoder-network resnet vgg


Function Point Analysis

Function Point Analysis and Software Sizing

Function point analysis (FPA) quantifies software size by counting business functions and user interactions independent of technology or programming language. Introduced by Albrecht (1979), FPA measures delivered functionality, enabling effort estimation, productivity benchmarking, and software value assessment. Organizations use FPA for project contracts, vendor comparison, and portfolio management.

Albrecht, A. J. (1979). Measuring application development productivity. In Proceedings of the IBM Applications Development Symposium (pp. 83–92).

International Function Point Users Group (2010). Function Point Analysis Counting Practices Manual. IFPUG.

Jones, C. (2008). Applied Software Measurement: Global Analysis for Improving Software Productivity and Quality (3rd ed.). McGraw-Hill.

use-case-point-estimation agile-velocity-tracking technical-debt-measurement software-complexity-metrics


FACIT-Sp

Amy H. Peterman, George Fitchett, Mark J. Brady, Lisette Hernandez, & David Cella

The FACIT-Sp, developed by Peterman and colleagues in 2002, is a 12-item self-report measure of spiritual well-being specifically designed for people with serious illness, particularly cancer. It assesses two dimensions: meaning and peace (the sense that life has purpose and harmony despite illness) and faith (spiritual or religious trust). Part of the Functional Assessment of Chronic Illness Therapy (FACIT) suite, the FACIT-Sp has become a standard measure in oncology research and palliative care, predicting quality of life, treatment outcomes, and psychological well-being in medical populations.

Peterman, A. H., Fitchett, G., Brady, M. J., Hernandez, L., & Cella, D. (2002). Measuring spiritual well-being in people with cancer: The Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being Scale. Annals of Behavioral Medicine, 24(1), 49–58.

daily-spiritual-experience-scale brief-religious-coping-scale systems-belief-inventory existential-wellbeing-scale


Functional Behavioral Assessment

Functional Behavioral Assessment Protocol

FBA
behavioral assessment
functional analysis

Functional Behavioral Assessment (FBA) is a systematic process for identifying the environmental and behavioral factors that maintain or contribute to a target behavior. Developed by Richard O'Neill, Robert Horner, and colleagues in the 1990s, FBA is a cornerstone of applied behavior analysis and is widely used in educational, clinical, and organizational settings to guide evidence-based behavior intervention planning.

O'Neill, R. E., Horner, R. H., Albin, R. W., Sprague, J. R., Storey, K., & Newton, J. S. (1997). Functional assessment and program development for problem behavior: A practical handbook (2nd ed.). Brooks/Cole Publishing.

Sugai, G., Horner, R. H., & Gresham, F. M. (2002). Behaviorally effective school environments. In M. R. Shinn, H. M. Walker, & G. Stoner (Eds.), Interventions for academic and behavior problems II: Preventive and remedial approaches. National Association of School Psychologists.


Functional Diversity

functional traits
trait diversity
ecological niche
functional space

Functional diversity quantifies the range and abundance distribution of functional traits (morphology, physiology, behavior) among species in a community. Developed by Mouillot and colleagues (2008), functional diversity indices measure how different species are in their ecological roles and resource use strategies. Unlike species richness (number of species), functional diversity captures the breadth of ecological strategies, predicting ecosystem function and stability.

Villéger, S., Mason, N. W., & Mouillot, D. (2008). New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology, 89(8), 2290-2301.

Mason, N. W., Mouillot, D., Lee, W. G., & Wilson, J. B. (2005). Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos, 111(1), 112-118.

Petchey, O. L., & Gaston, K. J. (2002). Functional diversity (FD), species richness and community composition. Ecology Letters, 5(3), 402-411.

species-accumulation indicator-value beta-diversity-partitioning food-web-topology


Functional Group Identification

Functional Group Identification and Characterization

functional group analysis
FG identification
structural analysis

Functional group identification is the systematic determination of chemical functional groups present in organic molecules using spectroscopic, chemical, and structural data. Developed throughout the 20th century alongside spectroscopy and analytical chemistry, this methodology enables rapid structure elucidation by focusing on reactive moieties (alcohols, aldehydes, carboxylic acids, amines, etc.) rather than complete structure determination.

Clayden, J., Greeves, N., Warren, S., & Wothers, P. (2012). Organic Chemistry (2nd ed.). Oxford University Press.

Silverstein, R. M., Webster, F. X., Kiemle, D. J., & Bryce, D. L. (2014). Spectrometric Identification of Organic Compounds (8th ed.). John Wiley & Sons.


Functional Independence Measure

The Functional Independence Measure (FIM) is an 18-item standardized assessment of functional status and disability that measures the level of assistance required for activities of daily living (ADLs) and mobility in individuals with disabilities. Developed by Granger and Hamilton in the 1980s, the FIM has become a standard outcome measure in rehabilitation medicine, enabling comparison across facilities and tracking recovery progress from admission through discharge.

Granger, C. V., Hamilton, B. B., Keith, R. A., Zielezny, M., & Sherwin, F. S. (1986). Advances in functional assessment for medical rehabilitation. Topics in Geriatric Rehabilitation, 1(3), 59-74.

Hamilton, B. B., Laughlin, J. A., Fiedler, R. C., & Granger, C. V. (1994). Interrater reliability of the 7-level functional independence measure (FIM). Scandinavian Journal of Rehabilitation Medicine, 26(3), 115-119.


FLIC

The Functional Living Index-Cancer is a 22-item patient self-report instrument that measures health-related quality of life in cancer patients across physical, social, emotional, and overall QoL domains. Developed by Schipper and colleagues in the mid-1980s, the FLIC was among the first disease-specific QoL instruments for cancer and served as a foundational model for subsequent comprehensive measures like the EORTC QLQ-C30, bridging early generic QoL concepts with cancer-specific measurement.

Patient self-report functional living and quality-of-life scale

Schipper, H., Clinch, J., & Olweny, C. L. M. (1996). Quality of life studies: definitions and conceptual issues. In B. Spilker (Ed.), Quality of life and pharmacoeconomics in clinical trials (pp. 11–23). Lippincott-Raven.

Aaronson, N. K., Ahmedzai, S., Bergman, B., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst, 85(5), 365–376.

fact-g fact-b-breast-cancer edmonton-symptom-assessment distress-thermometer memorial-symptom-assessment-scale


Functional Ultrasound

fUS
doppler ultrasound
ultrafast ultrasound

Functional Ultrasound (fUS) is a high-framerate Doppler ultrasound technique that dynamically maps blood flow and hemodynamic changes in vivo with millisecond temporal resolution. Pioneered by Tanter, Macé, and colleagues in the 2010s, fUS enables real-time imaging of microvascular perfusion in the brain and other organs. By combining ultrafast acquisition (1000-5000 frames per second) with Doppler processing, fUS reveals functional activity (hemodynamic changes during stimulation or behavior) and vascular networks with unprecedented spatiotemporal detail.

High-framerate doppler imaging for hemodynamics

Macé, E., Montaldo, G., Trenholm, S., et al. (2011). Functional ultrasound imaging of the brain. Nature Methods, 8(8), 662-664.

Tiran, E., Sieu, L. A., Bergel, A., et al. (2017). Multiplane wave imaging increases signal awareness for small vasculature imaging in mice and rats. IEEE Transactions on Medical Imaging, 36(11), 2371-2379.

Errico, C., Pierre, J., Pezet, S., et al. (2015). Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging. Nature, 527(7579), 499-502.

oct-angiography dti-tractography pet-kinetic-modeling quantitative-susceptibility-mapping magnetic-resonance-elastography


Fuzzing

Fuzzing is a software testing technique that inputs large numbers of random or semi-random test cases to a program to find bugs, crashes, and security vulnerabilities. Pioneered by Barton Miller in 1990, fuzzing has become a primary method for discovering zero-day vulnerabilities in complex software. Modern fuzzing tools like libFuzzer, AFL, and HoneyPot combine coverage-guided mutation with instrumentation to efficiently explore program paths and trigger vulnerabilities. Fuzzing has discovered thousands of critical vulnerabilities in major software including browsers, compilers, and cryptographic libraries.

Miller, B. P., Fredriksen, L., & So, B. (1990). An empirical study of the reliability of UNIX utilities. Communications of the ACM, 33(12), 32-44.

Böhme, M., Pham, V. T., Sharma, A., & Cichon, M. (2020). Fuzzing: Challenges and reflections. IEEE Security & Privacy, 19(2), 56-62.


FUZZY-AHP

FUZZY-AHP (Fuzzy AHP — Fuzzy extension of the Analytic Hierarchy Process) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Van Laarhoven, P. J. M., Pedrycz, W. in 1983. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise comparison with Triangular Fuzzy Number (TFN: l, m, u) judgments — umbrella over Van Laarhoven-Pedrycz 1983, Buckley 1985, Chang 1992 extent analysis, Cheng 1996 entropy-based

Van Laarhoven, P. J. M., Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems

fuzzy-aras fuzzy-aroman fuzzy-cocoso fuzzy-codas fuzzy-copras fuzzy-dnma fuzzy-edas fuzzy-electre-i


Fuzzy ANOVA

Fuzzy ANOVA extends classical analysis of variance to fuzzy data where observations and group memberships are imprecise or uncertain. Developed by Viertl and others, Fuzzy ANOVA tests whether fuzzy-valued groups differ significantly while accounting for inherent measurement uncertainty.

Viertl, R. (2011). Statistical Methods for Fuzzy Data. Wiley.

Ferrari, G., Ayyalasomayajula, R., & Fabbri, R. (2018). Fuzzy ANOVA: A comparison of quantile-based, and fuzzy-ratio approaches. SORT: Statistics and Operations Research Transactions, 42(1), 93-120.

Parchami, A., Noori, H., & Mashinchi, M. (2013). Fuzzy confidence intervals for mean of fuzzy random variables. Expert Systems with Applications, 40(18), 7154-7161.


FUZZY-ARAS

FUZZY-ARAS (Fuzzy ARAS — Fuzzy extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Turskis, Z., Zavadskas, E. K. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)

Turskis, Z., Zavadskas, E. K. (2010). A new fuzzy additive ratio assessment method (ARAS–F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport


FUZZY-AROMAN

FUZZY-AROMAN (Fuzzy AROMAN — Fuzzy extension of AROMAN) is a ranking multi-criteria decision-making (MCDM) method introduced by Bošković et al. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Bošković et al. (2023). Fuzzy Alternative Ranking Order Method Accounting for two-step Normalization. IEEE Access


FUZZY-BWM

FUZZY-BWM (Fuzzy BWM — Triangular Fuzzy Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Guo, S., Zhao, H. in 2015 crisp; 2017 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Triangular-fuzzy Best-to-Others and Others-to-Worst pairwise comparison with nonlinearly-constrained programming

Guo, S., Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems


Fuzzy C-Means

FCM
fuzzy clustering
soft k-means
bulanık c-ortalama kümeleme

Fuzzy C-Means is a soft clustering algorithm in which every data point belongs to every cluster with a graded membership between 0 and 1, rather than being assigned to exactly one cluster. Originated by Joseph Dunn in 1973 and generalized by James Bezdek in 1981, it minimizes a fuzzy-weighted within-cluster variance, making it well suited to data whose groups overlap or have no sharp boundaries.

Graded (each point belongs to all clusters by degree)

Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.

Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press.

k-means-clustering gaussian-mixture-model granular-computing spectral-clustering


FUZZY-COCOSO

FUZZY-COCOSO (Fuzzy CoCoSo — Fuzzy extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems


FUZZY-CODAS

FUZZY-CODAS (Fuzzy CODAS — Fuzzy extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., Antucheviciene, J. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., Antucheviciene, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management


Fuzzy Cognitive Maps

FCM
Kosko cognitive map
causal cognitive map
bulanık bilişsel haritalar

A fuzzy cognitive map, introduced by Bart Kosko in 1986, represents a system as a network of concepts connected by signed, weighted causal links, and simulates how the concepts influence one another over time. By combining the intuitive structure of a cognitive map with fuzzy weights and iterative activation, FCMs let experts encode causal knowledge and then run what-if scenarios — making them popular for policy analysis, strategic decision-making, and modelling complex socio-technical systems.

Fuzzy causal/feedback network for scenario analysis

Signed weighted causal relations between concepts

Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.

Papageorgiou, E. I., & Salmeron, J. L. (2013). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21(1), 66–79.

bayesian-network system-dynamics dempster-shafer-theory agent-based-modeling


FUZZY-COPRAS

FUZZY-COPRAS (Fuzzy COPRAS — Fuzzy extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-DELPHI

FUZZY-DELPHI (Fuzzy Delphi — Expert consensus with triangular fuzzy opinions and defuzzification) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Kaufmann, A., Gupta, M. M. in 1988. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fuzzy expert elicitation — TFN Delphi with centroid defuzzification

Kaufmann, A., Gupta, M. M. (1988). Fuzzy Mathematical Models in Engineering and Management Science. Elsevier Science Publishers, Amsterdam


FUZZY-DNMA

FUZZY-DNMA (Fuzzy DNMA — Fuzzy extension of DNMA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-EDAS

FUZZY-EDAS (Fuzzy EDAS — Fuzzy extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-ELECTRE-I

FUZZY-ELECTRE-I (Fuzzy ELECTRE I (Group, Trapezoidal)) is a outranking multi-criteria decision-making (MCDM) method introduced by Hatami-Marbini, A., Tavana, M. in 1968 crisp; 2011 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Group fuzzy outranking — Trapezoidal Fuzzy Number (TrFN: l, p, q, u)

Hatami-Marbini, A., Tavana, M. (2011). An extension of the Electre I method for group decision-making under a fuzzy environment. Omega


FUZZY-ELECTRE-II

FUZZY-ELECTRE-II (Fuzzy ELECTRE II — Fuzzy extension of ELECTRE-II) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. & Bertier, P. (canonical crisp); Govindan, K., Grigore, M.C. & Kannan, D. (TFN applicator) in 1973 crisp; 2010 TFN applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Govindan, K., Grigore, M.C., Kannan, D. (2010). Ranking of third party logistics provider using fuzzy ELECTRE II. The 40th International Conference on Computers & Industrial Engineering (CIE40)


FUZZY-ELECTRE-III

FUZZY-ELECTRE-III (Fuzzy ELECTRE III — Fuzzy extension of ELECTRE-III) is a outranking multi-criteria decision-making (MCDM) method introduced by Montazer, G. A., Qahri Saremi, H., Ramezani, M. (precursor: Qahri Saremi & Montazer 2007 WSEAS) in 2009. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Montazer, G. A., Qahri Saremi, H., Ramezani, M. (2009). Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection. Expert Systems with Applications


FUZZY-FMEA-DEA

FUZZY-FMEA-DEA (Fuzzy Smart FMEA with DEA (FSFMEA) — Fuzzy FMEA + CCR Data Envelopment Analysis for corrective action prioritization) is a ranking multi-criteria decision-making (MCDM) method introduced by Adesina, K.A.; Yazdi, M.; Zarei, E.; Pouyakian, M. (FSFMEA hybrid); Stamatis, D.H. 2003 (FMEA reference); Charnes-Cooper-Rhodes 1978 (DEA CCR); Buckley, J.J. 1985 (Fuzzy AHP for expert weighting) in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Risk-based ranking — Fuzzy FMEA (S×O×D under TFN/TrFN) + DEA CCR efficiency scoring for corrective action prioritization

Adesina, K. A., Yazdi, M., Zarei, E., Pouyakian, M. (2022). Smart Decision Fuzzy-Based Data Envelopment Model for Failure Modes and Effects Analysis. in: Yazdi M. (ed.), Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis, Studies in Fuzziness and Soft Computing, Vol. 414, Springer, Cham, pp. 151-168 (Chapter 7)


FUZZY-INFORMATION-AXIOM

FUZZY-INFORMATION-AXIOM (FIA — Fuzzy Information Axiom (Axiomatic Design under fuzzy data)) is a ranking multi-criteria decision-making (MCDM) method introduced by Kahraman, C., Kulak, O. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Information content minimization — axiomatic design with TFN system/design ranges, common-area / system-area ratio

Kahraman, C., Kulak, O. (2008). Fuzzy Multi-Attribute Decision Making Using an Information Axiom-Based Approach. In: Kahraman, C. (ed.), Fuzzy Multi-Criteria Decision Making, Springer LNEMS


FUZZY-MABAC

FUZZY-MABAC (Fuzzy MABAC — Fuzzy extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-MARCOS

FUZZY-MARCOS (Fuzzy MARCOS — Fuzzy extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-MAUT

FUZZY-MAUT (Fuzzy MAUT — Fuzzy extension of MAUT) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-MOORA

FUZZY-MOORA (Fuzzy MOORA — Fuzzy extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-MULTIMOORA

FUZZY-MULTIMOORA (Fuzzy MULTIMOORA — Fuzzy extension of MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-OCRA

FUZZY-OCRA (Fuzzy OCRA — Fuzzy extension of OCRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-PROMETHEE

FUZZY-PROMETHEE (Fuzzy PROMETHEE — Fuzzy extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Goumas, M., Lygerou, V. in 1986 crisp; 2000 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Goumas, M., Lygerou, V. (2000). An extension of the PROMETHEE method for decision making in fuzzy environment: Ranking of alternative energy exploitation projects. European Journal of Operational Research


FUZZY-PSI

FUZZY-PSI (Fuzzy PSI — Fuzzy extension of PSI) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-RAFSI

FUZZY-RAFSI (Fuzzy RAFSI — Fuzzy extension of RAFSI) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-RAWEC

FUZZY-RAWEC (Fuzzy RAWEC — Fuzzy extension of RAWEC) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


Fuzzy Regression Discontinuity in Education Research

Fuzzy Regression Discontinuity Design in Education Research

Fuzzy RDD
Fuzzy RD
Imperfect RDD
Non-sharp RD

Imbens & Lemieux (2008); applied in education by Jacob & Lefgren (2004) and Angrist & Lavy (1999)

Fuzzy Regression Discontinuity Design (Fuzzy RDD) is a quasi-experimental causal method that exploits a known score threshold — such as a test cutoff — to estimate the effect of a program or intervention when assignment is imperfect. Widely used in education research to evaluate summer school, remedial programs, scholarships, and class-size rules, it uses two-stage least squares to recover a local average treatment effect for students near the threshold.

Observational panel or cross-sectional data with a continuous running variable and a known threshold

Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.

Jacob, B. A., & Lefgren, L. (2004). Remedial education and student achievement: A regression-discontinuity analysis. Review of Economics and Statistics, 86(1), 226-244.

regression-discontinuity-design sharp-regression-discontinuity instrumental-variables difference-in-differences propensity-score-matching local-average-treatment-effect


Fuzzy Regression Discontinuity

Fuzzy RD
Fuzzy RDD
Fuzzy RD Design
Imperfect RDD

Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.

Cross-sectional or panel with a continuous running variable and imperfect compliance near a cutoff

Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.

regression-discontinuity-design instrumental-variables difference-in-differences propensity-score-matching local-average-treatment-effect two-stage-least-squares


FUZZY-ROV

FUZZY-ROV (Fuzzy ROV — Fuzzy extension of ROV) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-SAW

FUZZY-SAW (Fuzzy SAW (Bonissone 1982) - L-R trapezoidal Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Bonissone, P. P. in 1982. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fuzzy SAW - L-R trapezoidal (a, b, alpha, beta)

Bonissone, P. P. (1982). A fuzzy set based linguistic approach: Theory and applications. Approximate Reasoning in Decision Analysis (Gupta, M. M. and Sanchez, E., Eds.)


FUZZY-SIWEC

FUZZY-SIWEC (Fuzzy SIWEC — Fuzzy Simple Weight Calculation (F-SIWEC)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Triangular Fuzzy Number extension of SIWEC; experts use linguistic variables; dispersion-weighted aggregation with TFN arithmetic; outputs fuzzy weights defuzzified for downstream use.

Puška, A., Nedeljković, M., Pamučar, D., Božanić, D., Simić, V. (2024). Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products. MethodsX

fuzzy-rawec fuzzy-topsis fuzzy-vikor fuzzy-promethee fuzzy-edas fuzzy-marcos fuzzy-mabac fuzzy-aras


FUZZY-SPOTIS

FUZZY-SPOTIS (Fuzzy SPOTIS — Fuzzy extension of SPOTIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Shekhovtsov, A., Paradowski, B., Więckowski, J., Kizielewicz, B., Sałabun, W. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Shekhovtsov, A., Paradowski, B., Więckowski, J., Kizielewicz, B., Sałabun, W. (2022). Extension of the SPOTIS method for the rank reversal free decision-making under fuzzy environment. 2022 IEEE 61st Conference on Decision and Control (CDC)


FUZZY-TODIM

FUZZY-TODIM (Fuzzy TODIM — Fuzzy extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems


FUZZY-TOPSIS-CHEN2000 (Fuzzy TOPSIS (Chen 2000) — TOPSIS extension for group decision-making under fuzzy environment with triangular fuzzy numbers) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C.-T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems


FUZZY-TOPSIS

FUZZY-TOPSIS (Fuzzy TOPSIS (Chen-Hwang 1992) — Trapezoidal fuzzy TOPSIS with Zadeh sup-min similarity distance) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, S.-J., Hwang, C.-L. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance-based ranking — Trapezoidal Fuzzy Number (TrFN: a, b, c, d) with Zadeh max-min similarity

Chen, S.-J., Hwang, C.-L. (1992). Fuzzy Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 375, Springer-Verlag, Berlin


FUZZY-VIKOR

FUZZY-VIKOR (Fuzzy VIKOR — Fuzzy extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Opricovic, S. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Opricovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications


FUZZY-WASPAS

FUZZY-WASPAS (Fuzzy WASPAS — Fuzzy extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Turskis, Z., Zavadskas, E.K., Antucheviciene, J., Kosareva, N. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Turskis, Z., Zavadskas, E.K., Antucheviciene, J., Kosareva, N. (2015). A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection. International Journal of Computers Communications & Control


FUZZY-WISP

FUZZY-WISP (Fuzzy WISP — Fuzzy extension of WISP) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FUZZY-WPM

FUZZY-WPM (Fuzzy WPM (Kahraman-Birgun-Yenen 2008) - Triangular Fuzzy Weighted Product) is a ranking multi-criteria decision-making (MCDM) method introduced by Kahraman, C., Birgun, S., Yenen, V. Z. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fuzzy WP / Multiplicative Weighting - Triangular Fuzzy Number (l, m, u)

Kahraman, C., Birgun, S., Yenen, V. Z. (2008). Fuzzy Multi-Attribute Scoring Methods with Applications (Ch.7 Section 3.2 - Fuzzy Multiplicative Weighting Method). Fuzzy Multi-Criteria Decision Making (Kahraman, C., Ed.)


FxLMS Active Noise Control

Filtered-x Least Mean Squares Algorithm for Active Noise Control

FxLMS
filtered-x LMS
active noise cancellation
ANC

The Filtered-x Least Mean Squares (FxLMS) algorithm is an adaptive filter used in active noise control (ANC) systems to reduce unwanted sound by generating anti-noise. Pioneered by Widrow and Stearns in 1975 and refined by Eriksson and colleagues, FxLMS is the most widely deployed algorithm in commercial noise-canceling headphones, hearing aids, automotive cabins, and industrial noise barriers. It works by continuously learning the acoustical path and dynamically adjusting a canceling signal in real time.

Widrow, B., & Stearns, S. D. (1975). Adaptive signal processing for active vibration and noise control. IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(5), 440–453.

Eriksson, L. J., Allie, M. C., & Greiner, R. A. (1988). The selection and application of an IIR adaptive filter for use in active sound attenuation. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(11), 1879–1891.

Kuo, S. M., & Morgan, D. R. (2002). Active Noise Control Systems: Algorithms and DSP Implementations. John Wiley & Sons.

psychoacoustic-masking speech-intelligibility beamforming linear-predictive-coding cepstral-analysis


G-Computation

G-formula
Parametric G-formula
Standardization

G-computation is a causal inference method for estimating the effect of an intervention or treatment on an outcome from observational data. Developed by James M. Robins in 1986, it provides a parametric approach to standardization that can handle time-varying exposures and confounders. The method estimates what the population outcome would be under different intervention scenarios by utilizing fitted outcome models.

Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods: application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.

Taubman, S. L., Robins, J. M., Mittleman, M. A., & Hernán, M. A. (2009). Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. International Journal of Epidemiology, 38(6), 1599-1611.

Ahern, J., Hubbard, A., & Galea, S. (2009). Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. American Journal of Epidemiology, 169(9), 1140-1147.


G-Theory

Generalizability Theory
G-Study / D-Study framework
Genellenebilirlik Kuramı (G-Kuramı)

Generalizability Theory, developed by Lee J. Cronbach and colleagues in the 1960s and formalised by Brennan (2001), is an ANOVA-based framework that extends Classical Test Theory by decomposing observed score variance into multiple, separately identified sources of measurement error — such as raters, tasks, occasions, or items — rather than bundling all error into a single undifferentiated term.

G-coefficient (relative decisions) and Phi-coefficient (absolute decisions)

Continuous or ordinal scores from multi-facet measurement designs

Brennan, R. L. (2001). Generalizability Theory. Springer.

Shavelson, R. J. & Webb, N. M. (1991). Generalizability Theory: A Primer. Sage.

icc-intraclass-correlation cronbach-alpha rasch-model cfa-psychometric two-pl-irt interrater-reliability


Generalized Anxiety Disorder-7

Robert L. Spitzer, Kurt Kroenke, Janet B. Williams, Bernd Löwe

The GAD-7 is a brief 7-item self-report questionnaire designed to screen for and measure the severity of generalized anxiety disorder in adolescents and adults. Developed by Spitzer, Kroenke, Williams, and Löwe in 2006, it has become one of the most widely used anxiety screening tools in primary care, mental health research, and clinical practice due to its brevity, excellent psychometric properties, and ease of administration.

Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092-1097.


Gale-Shapley Algorithm

Stable Marriage Problem
Deferred Acceptance
Two-Sided Matching

The Gale-Shapley algorithm solves the stable marriage problem: how to match two groups (e.g., medical residents to hospitals, students to schools) such that no pair prefers each other to their assigned partners. Introduced by David Gale and Lloyd Shapley in 1962, the algorithm guarantees a stable matching in polynomial time through a deferred acceptance process where one side proposes sequentially and the other side responds, revising choices as better options arrive.

Gale, D., & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1), 9-15.

Roth, A. E. (1984). The economics of matching: Stability and incentives. Mathematics of Operations Research, 7(4), 617-628.

bayesian-nash-equilibrium vcg-mechanism principal-agent-model top-trading-cycles


Galerkin Method

Galerkin Method for Finite-Dimensional Approximation

Bubnoff-Galerkin
weighted residual method
projection method

The Galerkin Method is a projection-based variational technique for solving differential equations by reducing infinite-dimensional problems to finite-dimensional linear systems. Developed by Boris Galerkin in 1915 and independently by Ivan Bubnoff, it underpins the Finite Element Method (FEM) and is foundational to modern computational engineering.

Galerkin, B. G. (1915). Elastic plates and shells. Proceedings of Higher Technical School, Moscow.

Bubnoff, I. G. (1913). The application of the method of integral equations to the solutions of problems of elastic equilibrium of shells. Izvestiya Rossiiskoi Akademii Nauk, 4, 1311–1330.

Reddy, J. N. (1993). An Introduction to the Finite Element Method (2nd ed.). McGraw-Hill.

finite-element-method spectral-methods discontinuous-galerkin weighted-residual-method


PGSI

Problem Gambling Severity Index
PGSI
Gambling Disorder Screen

The PGSI (Problem Gambling Severity Index) is a 9-item self-report questionnaire measuring problem gambling severity and gambling disorder risk. Developed by Ferris and Wynne in 2001 for the Canadian Centre on Substance Use and Addiction, it is one of the most widely used screening tools for gambling disorder in English-speaking countries. The PGSI assesses gambling frequency, loss of control, negative consequences, and harm from gambling. It is available freely and has been translated into multiple languages.

Ferris, J. A., & Wynne, H. J. (2001). The Canadian problem gambling index: Final report. Ottawa: Canadian Centre on Substance Use and Addiction.

Wynne, H. J. (2003). Gambling and problem gambling in Saskatchewan. Final report of the 2003 Saskatchewan Gambling Prevalence Survey. Regina: Saskatchewan Alcohol and Drug Commission.

Williams, R. J., Volberg, R. A., & Stevens, R. M. (2012). The population prevalence of problem gambling: Methodological influences, standardized rates, jurisdictional differences, and worldwide trends. Journal of Gambling Studies, 28(2), 112–128.


Games-Howell Test

Games-Howell post-hoc
Games-Howell procedure
Games-Howell Post-Hoc Testi

The Games-Howell test is a parametric post-hoc multiple comparison procedure that identifies which pairs of group means differ significantly after an omnibus ANOVA reveals a significant overall effect. Proposed by Games and Howell in 1976, it is specifically designed for situations where group variances and/or sample sizes are unequal, making it the recommended alternative to Tukey HSD whenever Levene's test signals heteroscedasticity.

Games, P. A. & Howell, J. F. (1976). Pairwise multiple comparison procedures with unequal N's and/or variances: A Monte Carlo study. Journal of Educational Statistics, 1(2), 113–125.

one-way-anova welch-anova tukey-hsd bonferroni-correction kruskal-wallis


GAMLSS

Generalized Additive Models for Location, Scale and Shape (GAMLSS)

Konum, Ölçek ve Şekil için Genelleştirilmiş Toplamlı Modeller

GAMLSS is a broad class of semi-parametric regression models introduced by Robert Rigby and Mikis Stasinopoulos in 2005. Unlike classical regression, which models only the mean of a response, GAMLSS allows each parameter of a chosen parametric distribution — location (e.g., mean), scale (e.g., variance), and shape (e.g., skewness, kurtosis) — to be modeled as an additive function of covariates. This makes it possible to capture heteroscedasticity, skewness, and heavy tails simultaneously within a single unified framework.

Semi-parametric distributional regression model

Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C, 54(3), 507–554.


Gamma Regression

Gamma Regression (Generalized Linear Model)

gamma GLM
gamma generalized linear model
Gamma Regresyonu (GLM)

Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear regression for variables such as health-care costs, durations, and income.

McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.

ols-regression poisson-regression negative-binomial-regression logistic-regression tweedie-regression


Gap Statistic

Robert Tibshirani, Guenther Walther, Trevor Hastie

The Gap Statistic, developed by Tibshirani, Walther, and Hastie in 2001, is a principled statistical method for determining the optimal number of clusters in a dataset. It compares the observed within-cluster sum of squares to the expected value under a null hypothesis of no clustering structure, providing a theoretically grounded approach to cluster number selection.

Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.

silhouette-score elbow-method calinski-harabasz-index davies-bouldin-index inertia


GARCH-MIDAS

GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by Engle and Ghysels (2012), this framework elegantly separates volatility time scales. The approach is powerful for understanding how macro conditions (growth, inflation) drive risk premia and for improved volatility forecasting.

Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391.

Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 674-704.


GARCH Model

Generalized Autoregressive Conditional Heteroskedasticity Model

GARCH
GARCH(1,1)
conditional volatility model
GARCH Modeli (Oynaklık Tahmini)

The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.

arima egarch simple-exponential-smoothing ols-regression quantile-regression


GARCH

GARCH(1,1)
generalized ARCH
conditional volatility model
GARCH Modeli

GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.

egarch gjr-garch dcc-garch arima simple-exponential-smoothing


Gas Chromatography-Olfactometry

Gas Chromatography-Olfactometry (GC-O) combines the separation power of gas chromatography with human olfactory perception to identify which volatile compounds in a food sample contribute to its aroma. Developed by Acree and colleagues in the 1990s, GC-O allows researchers to bypass the human nose's inability to consciously identify which of many simultaneous odors they are perceiving, replacing the 'olfactory bulb' with a trained human panelist.

Acree, T. E. (1997). GC/Olfactometry. Analytical Chemistry, 69(5), 170A-175A.

Delahunty, C. M., Eyres, G., & Dufour, J.-P. (2006). Gas chromatography-olfactometry. Journal of Separation Science, 29(14), 2107-2125.


Gated Recurrent Unit

The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734.

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. NIPS 2014 Deep Learning Workshop.

long-short-term-memory recurrent-neural-network transformer convolutional-neural-network bert-based-classification


Gaussian Mixture Model

Gaussian Karışım Modeli (GMM Kümeleme)
GMM
GMM clustering
mixture of Gaussians
model-based clustering

A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation.

Probabilistic (soft) clustering — mixture model

Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22.

kmeans-clustering pca umap-reduction hierarchical-clustering dbscan


Gaussian Process

Gaussian Process Regression and Classification

GP
Gaussian Process Regression
GPR
Kriging

A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

bayesian-optimization k-nearest-neighbors support-vector-machine linear-regression random-forest bayesian-gaussian-process


Gastroparesis Cardinal Symptom Index

Revicki, D. A., Rentz, A. M., Dubois, D., et al.

The Gastroparesis Cardinal Symptom Index (GCSI) is a validated, patient-reported outcome measure specifically designed to assess symptom severity in gastroparesis. Developed by Revicki and colleagues in 2003, the GCSI captures the three cardinal symptom clusters of gastroparesis: nausea and vomiting, postprandial fullness, and early satiety, plus bloating and stomach distension. The 9-item questionnaire is responsive to treatment changes and is increasingly used in clinical trials and practice to monitor gastroparesis progression and therapy response.

Revicki, D. A., Rentz, A. M., Dubois, D., Kahrilas, P., Stanghellini, V., Talley, N. J., & Tack, J. (2003). Development and validation of a patient-assessed gastroparesis symptom severity index. Alimentary Pharmacology & Therapeutics, 18(1), 141–150.

rome-iv-ibs-criteria gerd-hrql pac-qol child-pugh-score


GCTA

Genome-wide Complex Trait Analysis for Heritability Estimation

GREML
Genome-wide complex trait analysis
Heritability estimation

GCTA (Genome-wide Complex Trait Analysis) is a computational toolkit for estimating heritability and genetic correlations from genome-wide genotype and phenotype data. Developed by Yang and Visscher in 2011, GCTA uses genome-wide restricted maximum likelihood (GREML) to partition phenotypic variance into components explained by common SNPs, environmental factors, and residual variation. GCTA has become a standard tool for understanding the proportion of trait variation attributable to genetics across complex diseases and quantitative traits.

Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: A tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88(1), 76–82.

Zhou, X., Stephens, M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nature Genetics, 44(7), 821–824.

Pitchford, W. S., & Brown, W. M. (2019). Genomic prediction and selection of genomic variance. Genetics Selection Evolution, 51(1), 53–66.

polygenic-risk-score qtl-mapping ld-block-analysis f-statistics


Geriatric Depression Scale

Jerome A. Yesavage, Terry L. Brink, and colleagues

The Geriatric Depression Scale (GDS) is a 30-item self-report depression screening instrument specifically designed for older adults. Developed by Yesavage, Brink, and colleagues in 1982, the GDS addresses the unique presentation of depression in aging populations, where symptoms may differ from younger adults. A validated 15-item short form (GDS-15) is widely used in primary care and community settings for rapid screening.

Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17(1), 37-49.

Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontologist, 5(3-4), 165-173.


Geant4 Simulation

Geant4 is a Monte Carlo simulation toolkit for the passage of particles through matter, developed by an international collaboration. It provides a comprehensive framework for modeling detector geometries, simulating particle interactions, and predicting detector responses, making it essential for designing and optimizing particle physics experiments.

Agostinelli, S., et al. (2003). Geant4 - a simulation toolkit. Nuclear Instruments and Methods in Physics Research Section A, 506(3), 250–303.

Allison, J., et al. (2006). Geant4 developments and applications. IEEE Transactions on Nuclear Science, 53(1), 270–278.

Geant4 Collaboration. (2016). Recent developments in Geant4. Nuclear Instruments and Methods in Physics Research Section A, 835, 186–225.


Geary's C

Geary's C Spatial Autocorrelation Statistic

Geary contiguity ratio
Geary's contiguity ratio
global spatial autocorrelation
Geary C mekânsal otokorelasyon

Geary's C is a global measure of spatial autocorrelation — whether nearby locations tend to have similar values — introduced by Roy Geary in 1954. Unlike Moran's I, which is built on the covariation of values around the mean, Geary's C is built on the squared differences between neighbouring values, making it more sensitive to local, short-range variation. Values below 1 indicate positive spatial autocorrelation (similar neighbours), near 1 indicate randomness, and above 1 indicate negative autocorrelation.

Global spatial autocorrelation statistic

Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–146.

Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion.

moran-s-i getis-ord-gi lisa spatial-lag-model


Geary contiguity ratio
Geary C statistic
spatial contiguity ratio
Geary's c

Geary's C is a global spatial autocorrelation statistic that measures whether nearby areal units share similar attribute values. Unlike Moran's I, it focuses on squared differences between adjacent pairs rather than cross-products of deviations from the mean, making it more sensitive to local dissimilarity and less influenced by global trends.

Areal (polygon/lattice) data with continuous attribute values

Geary, R. C. (1954). The Contiguity Ratio and Statistical Mapping. The Incorporated Statistician, 5(3), 115–145.

Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion Limited.

morans-i spatial-autocorrelation local-indicators-of-spatial-association local-gearys-c getis-ord-g semivariogram


Gender Bias Detection

Gender Bias Detection in NLP — Statistical and Embedding-Based Methods

Toplumsal Cinsiyet Yanlılığı Tespiti — NLP

Caliskan et al. (2017); Zhao et al. (2018)

Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora and language models. Grounded in benchmarks established by Caliskan et al. (2017) with the Word Embedding Association Test (WEAT) and Zhao et al. (2018) with the WinoBias dataset, these methods produce quantitative evidence of gender bias rather than qualitative impressions. They are widely applied in ethical AI research, media analysis, and fairness auditing of machine-learning systems.

corpora, language model outputs, coreference datasets

Output: Bias score, stereotype rate, or occupational co-occurrence imbalance

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018). Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. Proceedings of NAACL-HLT 2018.

sentiment-analysis text-classification bert-embeddings named-entity-recognition coreference-resolution


Gene Set Enrichment Analysis

GSEA
gene-set analysis
functional enrichment analysis
pathway-level enrichment

2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)

Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.

Functional genomics / enrichment analysis

Ranked gene list from expression data (RNA-seq, microarray) plus a gene-set database (e.g., MSigDB)

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.

Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D., & Groop, L. C. (2003). PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics, 34(3), 267–273.


General Circulation Model

A General Circulation Model (GCM), also called a Global Climate Model, is a three-dimensional numerical representation of the Earth's atmosphere, oceans, ice, and land surface that simulates physical processes governing weather and climate. Pioneered by Manabe and Wetherald in 1975, GCMs are the primary tools for understanding past climate, projecting future climate change, and investigating climate sensitivity to greenhouse gases and other forcings.

Deterministic coupled atmosphere-ocean simulation

Manabe, S., & Wetherald, R. T. (1975). The effects of doubling the CO2 concentration on the climate of a general circulation model. Journal of the Atmospheric Sciences, 32(1), 3-15.

IPCC (2021). Climate Change 2021: The Physical Science Basis. Sixth Assessment Report.


Generalizability Theory

G-theory
G-study / D-study framework
variance components reliability

Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam

Generalizability Theory is a psychometric framework that decomposes observed score variance into multiple sources — persons, items, raters, occasions, and their interactions — using analysis of variance. It replaces the single reliability coefficient of classical test theory with a family of coefficients that tell researchers how well scores generalize across different measurement conditions.

Scores from crossed or nested measurement designs (persons × items × raters × occasions)

Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley.

Brennan, R. L. (2001). Generalizability Theory. Springer.

cronbachs-alpha mcdonalds-omega test-retest-reliability confirmatory-factor-analysis item-response-theory multilevel-reliability-analysis


Generalized Additive Model

GAM
additive model
spline-based additive regression
Genelleştirilmiş toplamsal model

A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.

Semi-parametric additive regression model

Output: Sum of smooth functions of each predictor

Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.

Hastie, T. J., & Tibshirani, R. J. (1990). Generalized Additive Models. Chapman & Hall/CRC.

multiple-linear-regression regression-splines loess polynomial-regression


Generalized Anxiety Disorder Scale

Robert L. Spitzer, Kurt Kroenke, Janet B. W. Williams, Bernd Löwe

The Generalized Anxiety Disorder-7 (GAD-7) is a brief, 7-item self-report instrument for screening and assessing the severity of anxiety symptoms in primary care and mental health settings. Developed by Spitzer and colleagues in 2006, the GAD-7 mirrors the structure and validation approach of the widely successful PHQ-9 depression screen and has rapidly become a standard anxiety assessment tool.

Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097.

Swinson, R. P. (2006). The GAD-7 scale was accurate for diagnosing generalized anxiety disorder. Evidence-Based Medicine, 11(6), 184.


Generalized Least Squares

GLS
Aitken estimator
EGLS
feasible GLS
FGLS
weighted least squares
WLS

Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.

Best Linear Unbiased Estimator (BLUE) under non-scalar error covariance

OLS (Ω = σ²I), WLS (Ω diagonal), FGLS (Ω estimated)

Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.

Greene, W. H. (2003). Econometric Analysis (5th ed.). Prentice Hall.

Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.


Generalized Linear Model

GLM
generalized regression
exponential family regression
link-function model

John A. Nelder & Robert W. M. Wedderburn

The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.

Continuous, binary, count, proportion, or other exponential-family outcomes

Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.

McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall/CRC.

logistic-regression poisson-regression negative-binomial-regression ordinal-logistic-regression ols-regression generalized-additive-model


General Self-Efficacy Scale

GSE
Schwarzer Self-Efficacy
General Self-Efficacy

The General Self-Efficacy Scale (GSE) is a 10-item measure assessing beliefs in one's ability to handle difficult situations and to cope with challenges through adaptive effort. Developed by Ralf Schwarzer and Matthias Jerusalem in the mid-1990s, the GSE operationalizes self-efficacy as a generalized confidence in one's capacity to manage stressors across diverse situations, rather than task-specific confidence. The scale has become widely used in health psychology, occupational research, and studies examining resilience and adaptive coping.

Generalized self-efficacy and coping capacity measure

Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user's portfolio. Causal and control beliefs (pp. 35–37). NFER-Nelson.

Luszczynska, A., Scholz, U., & Schwarzer, R. (2005). The General Self-Efficacy Scale: Multicultural validation studies. The Journal of Psychology, 139(5), 439–457.

Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a new general self-efficacy scale. Organizational Research Methods, 4(1), 62–83.


Generalized Trust Scale

The Generalized Trust Scale measures an individual's propensity to trust people in general, particularly strangers with whom they have no direct relationship. Originally developed by Morris Rosenberg in 1956 and later refined by Toshio Yamagishi and colleagues, it has become foundational in research on social capital, civic participation, and intergroup relations.

Rosenberg, M. (1956). Misanthropy, political ideology, and political information. Public Opinion Quarterly, 20(2), 274-290.

Yamagishi, T., & Yamagishi, M. (1994). Trust and commitment in the United States and Japan. Motivation and Emotion, 18(2), 129-166.

Uslaner, E. M. (2002). The Moral Foundations of Trust. Cambridge University Press.

institutional-trust-scale social-cohesion-scale intergroup-contact-scale civic-engagement-scale community-belonging-scale


Generative Adversarial Network

Üretici Çekişmeli Ağ (GAN)
GAN
generative adversarial nets
adversarial network

A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.

Generative deep learning (adversarial two-network game)

Synthetic data generation, image synthesis, data augmentation

Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.

Karras, T. et al. (2020). Analyzing and Improving the Image Quality of StyleGAN. CVPR.

diffusion-model score-based-diffusion variational-autoencoder neural-ode principal-component-analysis


Genetic Algorithm

Genetic Algorithm — Evolutionary Optimization

Genetik Algoritma — Evrimsel Optimizasyon

A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.

Darwinian natural selection and genetics

Selection, crossover (recombination), mutation

Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.

Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley.

particle-swarm-optimization simulated-annealing differential-evolution nsga2 ant-colony-optimization


Genome-wide association study

GWAS
genome-wide association analysis
whole-genome association study
WGAS

Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)

A genome-wide association study (GWAS) systematically tests hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) across the human genome for statistical association with a trait or disease. By comparing allele frequencies between cases and controls — or by regressing SNP genotypes on a quantitative phenotype — GWAS identifies genomic loci that harbor common genetic variants contributing to complex traits. Since its large-scale debut in 2007, GWAS has catalogued thousands of robust disease–variant associations across virtually every common human condition.

SNP genotype array data or whole-genome sequencing data; binary or quantitative phenotype

Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678.

Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 years of GWAS discovery: Biology, function, and translation. American Journal of Human Genetics, 101(1), 5–22.

rna-seq-differential-expression eqtl-analysis epigenome-wide-association-study copy-number-variation-analysis variant-calling pathway-enrichment-analysis


Genre Analysis in Film

Film Genre Classification and Genre Evolution Analysis

film genre criticism
genre theory
genre conventions

Genre Analysis in Film is a method for systematically examining how films belong to and innovate within recognizable categories—horror, Western, science fiction, melodrama, comedy—each with characteristic conventions, visual styles, narrative structures, and ideological concerns. Developed through film studies by scholars like Rick Altman and Steve Neale, the method recognizes that film genres are not fixed natural categories but socially constructed, historically contingent systems that structure both film production and audience expectations. Genre analysis examines what conventions define a genre, how individual films conform to or challenge those conventions, how genres evolve over time, and what ideological work generic conventions perform.

Analytical method for identifying genre conventions, evolution, and ideological work in cinema

Altman, R. (1999). Film/Genre. British Film Institute.

Neale, S. (2000). Genre and Hollywood. Routledge.

Grant, B. K. (Ed.). (2007). Film Genre Reader III. University of Texas Press.

Todorov, T. (1975). The Fantastic: A Structural Approach to a Literary Genre. Cornell University Press.

film-narrative-analysis auteur-theory-analysis media-framing-analysis discourse-analysis-media visual-content-analysis


Geochronological Dating

radiometric dating
isotopic dating
age determination

Geochronological dating is the determination of absolute ages of rocks and minerals using the decay of radioactive isotopes. Pioneered by Rutherford and Soddy (1902), this method provides numerical anchors for geological timescales and enables quantitative understanding of geological processes. Modern techniques (K-Ar, Rb-Sr, U-Pb, 40Ar/39Ar) span from recent to ancient events and are essential for calibrating relative chronologies and assessing rates of geological change.

Dickin, A. P. (2005). Radiogenic Isotope Geology (2nd ed.). Cambridge University Press.

Faure, G., & Mensing, T. M. (2005). Isotopes: Principles and Applications (3rd ed.). John Wiley & Sons.

McDougall, I., & Harrison, T. M. (1999). Geochronology and Thermochronology by the 40Ar/39Ar Method (2nd ed.). Oxford University Press.

paleomagnetism-analysis stratigraphic-correlation basin-subsidence-analysis paleoenvironmental-analysis geochemical-analysis


Geographic Profiling

Geographic Profiling for Crime Location Analysis

spatial crime analysis
crime hotspot mapping

Geographic profiling is a spatial analysis method used in forensic investigation to locate offenders based on the locations of their crimes. Developed by David Canter in 1994, it combines geostatistics, probability theory, and crime pattern analysis to identify high-probability crime origin zones. The method has been widely adopted in law enforcement agencies across North America and Europe.

Canter, D. V., & Hammond, L. (1994). Picking up the pieces: The identification of glass sources in forensic enquiries. Journal of Forensic Sciences, 39(4), 1018-1034.

Rossmo, D. K. (2000). Geographic Profiling. CRC Press.

Levine, N. (2006). Crime mapping and the crackdown on gangs in Los Angeles. In Geographic Information Systems and Crime Analysis, pp. 65-87.


Geographic Regression Discontinuity

Geographic Regression Discontinuity Design

Geographic Regression Discontinuity (GRD) is a quasi-experimental design that exploits sharp geographic boundaries—borders, policy boundaries, or natural features—to estimate causal effects. Introduced by Dell (2010) and others, it compares outcomes on either side of a boundary where treatment changes abruptly, leveraging the idea that units on opposite sides of a border are otherwise similar. This approach yields credible causal estimates for spatially localized policies, institutional changes, and natural phenomena.

Dell, M. (2018). The persistent effects of Peru's mining mita. Econometrica, 78(6), 1863-1911.

Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.


Geographically Weighted PCA

Local PCA
Spatially Adaptive PCA
Geographically Weighted Factor Analysis
Yerel Coğrafi Ağırlıklı PCA

Paul Harris, Chris Brunsdon & Martin Charlton

Geographically Weighted Principal Component Analysis (GWPCA) is a local dimensionality-reduction method introduced by Harris, Brunsdon, and Charlton in 2011. It extends classical PCA by fitting a separate weighted PCA at every location in a dataset, allowing eigenstructures — the principal components and their loadings — to vary continuously across geographic space rather than being constrained to a single global solution. GWPCA is suited to researchers in environmental science, public health, and regional economics who suspect that multivariate relationships among variables differ by location.

Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717–1736.


Geographically Weighted Random Forest

Geographical Random Forest
GRF
Spatial Random Forest
Cografi Agirlikli Rastgele Orman

Geographically Weighted Random Forest (GWRF) is a spatially local ensemble learning method that fits an independent Random Forest model at each observation location, weighting nearby training samples more heavily than distant ones through a spatial kernel function. It was introduced by Stefanos Georganos and colleagues in 2019 (published 2021) as an extension of Breiman's Random Forest to handle spatial non-stationarity — the phenomenon where predictor–response relationships vary across geographic space.

Spatially local ensemble learning method

Gaussian or adaptive bisquare spatial kernel

Georganos, S., et al. (2021). Geographical random forests: a spatial extension of the random forest algorithm. Geocarto International, 36(2), 121–136.


Geographically Weighted Regression

Geographically Weighted Regression (GWR)

spatially varying coefficient regression

Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.

ols-regression spatial-lag-model spatial-error-model morans-i-test lisa-analysis


Geologic Mapping

field mapping
geological surveying
lithostratigraphic mapping

Geologic mapping is the systematic observation and documentation of rock types, structures, and relationships exposed on the land surface. Pioneered by William Smith in 1799, this foundational field method remains essential for understanding subsurface geology, economic geology, hazard assessment, and paleoenvironmental reconstruction. Modern mapping integrates field observations with satellite imagery, digital logs, and GIS technology to create comprehensive three-dimensional geological frameworks.

regional geological documentation pipeline

Compton, R. R. (1962). Manual of Field Geology. John Wiley & Sons.

Fossen, H. (2010). Structural Geology (2nd ed.). Cambridge University Press.

U.S. Geological Survey. (2017). Standards for Digital Geologic Maps. USGS Open-File Report 2017–1102.

stratigraphic-correlation seismic-reflection-interpretation petrographic-analysis rock-mass-classification geomorphological-analysis


Geomechanical Modeling

mechanical earth modeling
stress modeling
rock mechanics simulation

Geomechanical modeling is the numerical simulation of stress and deformation in rock masses, integrating rock properties, pressure conditions, and geometric constraints. Rooted in classical mechanics (Coulomb, Mohr) but modernized by finite element and finite difference methods, this approach is essential for well integrity assessment, reservoir compaction prediction, and stability evaluation of slopes and excavations. Models link subsurface geology to rock mechanical behavior.

Jaeger, J. C., & Cook, N. G. W. (1979). Fundamentals of Rock Mechanics (2nd ed.). Chapman and Hall.

Zoback, M. D. (2007). Reservoir Geomechanics. Cambridge University Press.

Fjær, E., Holt, R. M., Horsrud, P., Raaen, A. M., & Risnes, R. (2008). Petroleum Related Rock Mechanics (2nd ed.). Elsevier.

rock-mass-classification basin-subsidence-analysis well-log-analysis geophysical-inversion petrographic-analysis


Geometric Morphometrics

Geometric morphometrics is a quantitative analytical method that captures, analyzes, and compares the shapes of biological structures (bones, teeth, pottery) using coordinate data from landmarks and outlines. Developed by Fred Bookstein in the 1990s, GMM provides a rigorous statistical framework for studying shape variation across populations or time periods. The method allows archaeologists to quantify morphological differences between individuals, populations, or artifact classes with precision impossible using traditional linear measurements.

Bookstein, F. L. (1991). Morphometric Tools for Landmark Data: Geometry and Biology. Cambridge University Press.

Zelditch, M. L., Swiderski, D. L., Sheets, H. D., & Fink, W. L. (2004). Geometric Morphometrics for Biologists: A Primer. Elsevier.

Rohlf, F. J., & Slice, D. E. (1990). Extensions of the Procrustes method for the optimal superimposition of landmarks. Systematic Zoology, 39(1), 40-59.

dental-microwear-texture-analysis use-wear-analysis minimum-number-of-individuals number-of-identified-specimens


Geophysical Inversion

inverse problem solving
parameter estimation
model-data fitting

Geophysical inversion is the process of using observed geophysical data to estimate subsurface properties and structures. Formalized by Tikhonov (1963) and expanded by Tarantola (1987), this mathematical framework solves the inverse problem: given measurements (gravity, magnetics, seismic, electrical), what subsurface model produced them? Inversion is central to all quantitative geophysics and enables extraction of detailed subsurface information from surface or borehole measurements.

Tarantola, A. (1987). Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation. Elsevier.

Constable, S. C., Parker, R. L., & Constable, C. G. (1990). Occam's inversion: A practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics, 55(3), 289–300.

Menke, W. (2012). Geophysical Data Analysis: Discrete Inverse Theory (3rd ed.). Academic Press.

seismic-reflection-interpretation geophysical-survey basin-subsidence-analysis well-log-analysis geochemical-analysis


Geostrophic Velocity

Geostrophic Current
Thermal Wind Equation

Geostrophic velocity is the current driven by balance between the pressure gradient force and the Coriolis force, derived from the thermal wind equation. In most of the ocean away from the equator and coastal boundaries, geostrophic balance is an excellent approximation to the actual flow. Developed by Harald Sverdrup and colleagues in the 1940s, geostrophic velocity calculation from hydrographic data enables estimation of ocean currents without direct current measurements.

Sverdrup, H. U., Johnson, M. W., & Fleming, R. H. (1942). The Oceans: Their Physics, Chemistry, and General Biology. Prentice-Hall.

Vallis, G. K. (2006). Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-scale Circulation. Cambridge University Press.


Geostrophic Wind

Geostrophic wind
Geostrophic balance
Geostrophic approximation

Geostrophic wind balance is a fundamental concept in meteorology that describes the balance between the pressure gradient force and the Coriolis force in large-scale atmospheric flow. When this balance is achieved, wind blows parallel to isobars without acceleration—a condition observed in the free atmosphere away from the equator and surface boundary layer.

Holton, J. R. (2004). An Introduction to Dynamic Meteorology (4th ed.). Academic Press.

Held, I. M., & Hou, A. Y. (1980). Nonlinear axially symmetric circulations in a nearly inviscid atmosphere. Journal of the Atmospheric Sciences, 37(3), 515-533.


Velanovich, V., Zhang, Y., Hollis, J. B., et al.

The GERD Health-Related Quality of Life Scale (GERD-HRQL) is a concise, validated patient-reported outcome measure for assessing the symptomatic and functional impact of gastroesophageal reflux disease (GERD). Developed by Velanovich and colleagues in 1996, the 9-item GERD-HRQL measures heartburn frequency and severity, regurgitation, and impact on sleep and medication use. The scale is highly responsive to proton pump inhibitor (PPI) therapy and is widely used in GERD trials and clinical practice.

Velanovich, V., Zhang, Y., Hollis, J. B., Feldman, M. I., Sampliner, R., Guan, W., & Escamilla, C. (1996). Presenting symptoms and outcome measures in reflux esophagitis. Digestive Diseases and Sciences, 41(10), 1865–1873.

rome-iv-ibs-criteria pac-qol gcsi ibdq-short


GAI

The Geriatric Anxiety Inventory (GAI) is a 20-item self-report questionnaire developed by Pachana and colleagues in 2007 to assess anxiety symptoms specifically in older adults. Designed to address the limitations of general anxiety scales in detecting anxiety in older populations—where anxiety may present atypically or be masked by somatic complaints and medical comorbidities—the GAI focuses on cognitive and affective symptoms of anxiety with minimal emphasis on physical symptoms. It is widely used in geriatric practice, mental health clinics, and research to screen for and evaluate anxiety disorders in seniors.

Pachana, N. A., Byrne, G. J., Looi, J. C., Krishnan, V., & Hilbert, M. M. (2007). Development and validation of the Geriatric Anxiety Inventory. Int Psychogeriatr, 19(1), 103-114.

Byrne, G. J., & Pachana, N. A. (2011). Development and validation of a short form of the Geriatric Anxiety Inventory—the GAI-SF. Int Psychogeriatr, 23(1), 137-143.

Gerolimatos, L. A., Egan, J., & Stawasz, M. (2015). Associations between health status, cognitive status, and anxiety in older adults. Psychol Aging, 30(1), 75-88.

social-engagement-scale cognitive-telephone-screening life-space-assessment activities-balance-confidence short-physical-performance-battery


Germination Kinetics Modeling

seed germination modeling
thermal germination analysis
germination rate modeling
hydrothermal time modeling

1970s–1990s (formalized thermal and hydrothermal time frameworks)

Multiple contributors (Hegarty 1973; Garcia-Huidobro et al. 1982; Bradford 1990)

Germination Kinetics Modeling is a quantitative method used in agronomy, seed science, and crop physiology to describe, predict, and compare the speed and uniformity of seed germination under varying environmental conditions. It draws on thermal time and hydrothermal time frameworks to link temperature, water potential, and time into biologically interpretable parameters, enabling researchers and agronomists to characterize seed lot quality and optimize planting conditions.

Quantitative modeling / biophysical analysis

Time-to-event germination counts under controlled temperature and/or water potential conditions

Bradford, K. J. (2002). Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Science, 50(2), 248–260.

Bewley, J. D., & Black, M. (1994). Seeds: Physiology of Development and Germination (2nd ed.). Plenum Press.

dose-response-modeling survival-analysis nonlinear-regression thermal-time-analysis plant-growth-modeling probit-analysis


GES Algorithm

Greedy Equivalence Search
GES Causal Discovery
Score-Based Greedy Search
Açgözlü Eşdeğerlik Araması

Greedy Equivalence Search (GES) is a score-based algorithm for learning the causal structure of a set of variables from observational data. Introduced by David Maxwell Chickering in 2002, GES operates directly on Markov equivalence classes of directed acyclic graphs (DAGs), represented as completed partially directed acyclic graphs (CPDAGs). Under the assumptions of causal sufficiency and a faithful data-generating process, GES is proven to recover the true equivalence class in the large-sample limit.

Score-based causal structure learning algorithm

Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3, 507–554.


Gestalt Principles Analysis

Perceptual Organization Evaluation
Visual Grouping Assessment

Perceptual psychology and visual organization

Gestalt Principles Analysis is a framework for evaluating how visual elements are organized and grouped within a design or image. Originating in early twentieth-century perceptual psychology, this method assesses how principles like proximity, similarity, continuity, and closure guide viewers' perception of coherent wholes rather than disconnected parts.

Wertheimer, M. (1923). Untersuchungen zur Lehre von der Gestalt. Psychologische Forschung, 4, 301–350.

Koffka, K. (1935). Principles of Gestalt Psychology. Harcourt, Brace and Company.

Wagemans, J., Elder, J. H., Kubovy, M., Palmer, S. E., Peterson, M. A., Singh, M., & von der Heydt, R. (2012). A Century of Gestalt Psychology in Visual Perception: I. Perceptual Grouping and Figure-Ground Organization. Psychological Bulletin, 138(6), 1172–1217.

visual-balance-measurement visual-complexity-measure visual-saliency-map color-harmony-analysis image-aesthetics-assessment