IVIF-COPRAS

IVIF-COPRAS (Interval-Valued Intuitionistic Fuzzy COPRAS (Davoudabadi-Mousavi-Mohagheghi-Vahdani 2019)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Compound-proportional ranking MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ⟨[μ⁻,μ⁺],[ν⁻,ν⁺]⟩; μ⁺+ν⁺ ≤ 1) — Xu normalization + IVIF profit/cost sums + Garg score (GIS) compound Q_i + utility degree D_i (%)


IVIF-MABAC

IVIF-MABAC (Interval-Valued Intuitionistic Fuzzy MABAC (Xue, You, Lai, Liu 2016)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Border-approximation-area MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ([μ⁻,μ⁺],[ν⁻,ν⁺]); μ⁺+ν⁺ ≤ 1)

Atanassov, K. T., Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems

ahp anp bwm critic entropy if-entropy merec swara


IVIF-TODIM

IVIF-TODIM (Interval-Valued Intuitionistic Fuzzy TODIM (Krohling & Pacheco 2014)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Prospect-theory dominance MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ([μ⁻,μ⁺],[ν⁻,ν⁺]); μ⁺+ν⁺ ≤ 1)


IVIF-VIKOR

IVIF-VIKOR (Interval-Valued Intuitionistic Fuzzy VIKOR (Park, Cho & Kwun 2011)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Compromise-ranking MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ⟨[μ⁻,μ⁺],[ν⁻,ν⁺]⟩; μ⁺+ν⁺ ≤ 1) — Lp-metric distance to interval-valued intuitionistic PIS


IVN-MULTIMOORA

IVN-MULTIMOORA (MULTIMOORA under Interval-Valued Neutrosophic Number Environment) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkić, D. Zavadskas, E.K. Smarandache, F. Brauers, W.K.M. Karabašević, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Neutrosophic MULTIMOORA — Interval-Valued Neutrosophic Number (IVNN: <[tl,tu],[il,iu],[fl,fu]> with each component ∈[0,1], 0≤tu+iu+fu≤3)

Stanujkić, D., Zavadskas, E.K., Smarandache, F., Brauers, W.K.M., Karabašević, D. (2021). Cloud Computing Technology Selection Using a Novel Neutrosophic Extension of the MULTIMOORA Method. In: Smarandache, F., Abdel-Basset, M. (eds.) Neutrosophic Operational Research. Springer Nature Switzerland AG, Cham


Jaccard Index

Jaccard Similarity
Intersection over Union (IoU)

The Jaccard index measures the similarity between predicted and true label sets by computing the ratio of intersection to union. It is widely used in multi-label classification and set-based similarity tasks where partial overlap is important.

Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 547-579.

Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1-13.


Jackknife Estimation

delete-one jackknife
leave-one-out jackknife
Jackknife Yeniden Örnekleme

Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)

Jackknife estimation is a classical resampling technique that computes the bias and variance of a statistical estimator by systematically leaving out one observation at a time and re-computing the statistic on each reduced sample. Introduced by Maurice Quenouille in 1956 for bias correction and extended by John Tukey in 1958 who coined the name, it is the historical predecessor of the bootstrap and remains analytically tractable for smooth, differentiable estimators.

Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360.

Tukey, J. W. (1958). Bias and Confidence in Not Quite Large Samples. Annals of Mathematical Statistics, 29(2), 614.

bootstrap-estimation cross-validation delta-method permutation-test monte-carlo-simulation


Jackknife

leave-one-out resampling
Quenouille-Tukey jackknife
delete-one jackknife
Jackknife Yeniden Örnekleme

Quenouille (1956); reviewed by Miller (1974)

The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.

Resampling / bias and variance estimation

Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.

Miller, R. G. (1974). The Jackknife — A Review. Biometrika, 61(1), 1-15.

bootstrap-inference permutation-test mad-estimation robust-time-series ols-regression


Janka Hardness

The Janka hardness test measures wood resistance to indentation and denting by forcing a steel ball into the wood surface under standard load. Developed by Gabriel Janka in 1934, the test is a simple, nondestructive indicator of wood durability, wear resistance, and suitability for flooring, furniture, and other wear-prone applications. Janka hardness is one of the most widely used wood property metrics in wood science and commerce.

ASTM D1037-21. (2021). Standard test methods for evaluating properties of wood-base fiber and particle panel materials. ASTM International.

American Hardwood Export Council. (2012). Wood hardness ratings. Technical Report.


JHFT

The Jebsen-Taylor Hand Function Test (JHFT) is a standardized, performance-based measure of hand function developed to provide an objective, quantitative assessment of manual dexterity and hand capability. Created by Jebsen and colleagues (1969) at the University of Minnesota, the JHFT consists of seven timed functional hand tasks reflecting everyday hand activities. The JHFT is widely used in hand therapy, occupational therapy, and rehabilitation medicine to evaluate hand function in individuals with arthritis, hand injury, nerve compression syndromes, stroke, and other conditions affecting dexterity.

Performance-based, timed assessment by clinician

Jebsen, R. H., Taylor, N., Trieschmann, R. B., Trotter, M. J., & Howard, L. A. (1969). An objective and standardized test of hand function. Archives of Physical Medicine and Rehabilitation, 50(6), 311-319.

Hackel, M. E., Wolfe, G. A., Bang, S. M., & Canfield, J. S. (1992). Changes in hand function in the aging adult as determined by the Jebsen Test of Hand Function. Physical Therapy, 72(5), 373-377.

nine-hole-peg-test wolf-motor-function-test upper-extremity-functional-scale motor-assessment-scale


Jellyfish Search Optimizer

The Jellyfish Search Optimizer (JSO) is a biologically-inspired metaheuristic algorithm introduced by Shi et al. in 2022, based on the movement and foraging behavior of jellyfish in ocean environments. Jellyfish exhibit two distinct behaviors: passive drifting with ocean currents (exploration) and active swimming toward food sources (exploitation). JSO captures these behaviors to create an effective balance between global search and local refinement.

Shi, X., Sun, Y., Zhan, Z. H., Yuen, K. F., & Zhang, J. (2022). Jellyfish search optimizer: A new bio-inspired metaheuristic algorithm for solving optimization tasks. Neural Computing and Applications, 34(10), 7651-7673.

slime-mould-algorithm particle-swarm-optimization aquila-optimizer whale-optimization-algorithm salp-swarm-algorithm


Jensen-Shannon Divergence

JS divergence
symmetric KL divergence
JS distance

Jensen-Shannon divergence is a symmetric information-theoretic measure of the difference between two probability distributions. Developed by Jian Lin in 1991 as a refinement to the asymmetric Kullback-Leibler divergence, it overcomes KL's directional limitation by averaging the divergences in both directions. The result is a true metric (satisfying triangle inequality) that ranges from 0 (identical distributions) to 1, making it suitable for symmetric comparison tasks.

Symmetric probability distribution dissimilarity

Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.

Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. Wiley-Interscience.


JBI Critical Appraisal Tools

Joanna Briggs Institute Critical Appraisal Tools for Evidence Synthesis

JBI (Joanna Briggs Institute) Critical Appraisal Tools are a comprehensive suite of design-specific quality assessment instruments developed by the Joanna Briggs Institute (University of Adelaide, Australia) since 1998. Unlike single-tool approaches, JBI offers over 15 separate checklists tailored to RCTs, cohort studies, case-control studies, cross-sectional surveys, qualitative research, diagnostic accuracy, and economic evaluations. JBI tools are widely used in systematic reviews, particularly in healthcare, nursing, and public health.

Joanna Briggs Institute. (2017). Critical Appraisal Tools. University of Adelaide, South Australia. www.jbi.global/critical-appraisal-tools

casp-rct-checklist newcastle-ottawa-scale cosmin-checklist mmat


Job Content Questionnaire

Job Content Questionnaire (JCQ) - Job Strain Model

The Job Content Questionnaire (JCQ), developed by Robert Karasek in 1985, operationalizes the Job Strain Model, a foundational theory linking job characteristics to health outcomes. The JCQ measures job demands, decision latitude (autonomy and skill utilization), social support, and physical exertion. It identifies high-strain jobs (high demands, low control)—the most hazardous combination—and supports research linking work organization to cardiovascular disease, mental health, and occupational disability.

Karasek, R. A., Jr. (1985). Job Content Questionnaire and user's guide. Los Angeles: University of Southern California Department of Industrial and Systems Engineering.

Karasek, R., & Theorell, T. (1990). Healthy work: stress, productivity, and the reconstruction of working life. New York: Basic Books.

job-demands-resources-scale perceived-stress-scale job-satisfaction-survey emotional-exhaustion-scale work-ability-index


Job Demands-Resources Scale

Evangelia Demerouti and Arnold B. Bakker

The Job Demands-Resources Scale (JDRS) is a multidimensional assessment instrument based on the Job Demands-Resources (JD-R) model, developed by Demerouti and Bakker in 2001. It measures the balance between job demands (workload, time pressure, emotional demands) and resources (autonomy, support, opportunities for growth) that shape employee well-being, engagement, and burnout risk. The JDRS has become central to occupational health research and practice.

Bakker, A. B., & Demerouti, E. (2007). The Job Demands-Resources model: state of the art. Journal of Managerial Psychology, 22(3), 309-328.

Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The Job Demands-Resources model of burnout. Journal of Applied Psychology, 86(3), 499-512.

job-satisfaction-survey emotional-exhaustion-scale work-ability-index perceived-stress-scale organizational-commitment-scale


Job Descriptive Index

JDI
Smith Kendall Hulin Scale
Job Satisfaction Scale

The Job Descriptive Index (JDI) is a comprehensive self-report measure of job satisfaction across five distinct dimensions: work, supervision, coworkers, pay, and promotions. Developed by Smith, Kendall, and Hulin in 1969, it has become one of the most widely used and empirically validated job satisfaction instruments in organizational research. The JDI is prized for its multidimensional structure and strong psychometric properties.

Smith, P. C., Kendall, L. M., & Hulin, C. L. (1969). The measurement of satisfaction in work and retirement: A strategy for the study of attitudes. Rand McNally.

Balzer, W. K., Kihm, J. A., Smith, P. C., Irwin, J. L., Bachiochi, P. D., Robie, C., ... & Parra, L. F. (1997). Users' manual for the Job Descriptive Index (JDI) and the Job in General (JIG) Scales. Bowling Green State University.

Brodsky, A., Donovan, L. A., & Farh, J. L. (2011). A review of the Job Descriptive Index: A review of literature 2009-2011. In Handbook of employee satisfaction. Springer.


Job Satisfaction Survey

The Job Satisfaction Survey (JSS) is a 36-item, multidimensional self-report questionnaire developed by Paul Spector in 1985. It assesses nine facets of job satisfaction including pay, promotion, supervision, work itself, fringe benefits, coworkers, communication, working conditions, and management. The JSS has become one of the most widely used job satisfaction instruments in organizational research and practice.

Spector, P. E. (1985). Measurement of human service staff satisfaction: development of the Job Satisfaction Survey. American Journal of Community Psychology, 13(6), 693-713.

Spector, P. E. (1997). Job satisfaction: Application, assessment, causes, and consequences. Thousand Oaks, CA: SAGE Publications.

minnesota-satisfaction-questionnaire organizational-commitment-scale job-demands-resources-scale emotional-exhaustion-scale psychological-safety-scale


Job Shop Scheduling

Job shop scheduling is the problem of assigning a set of jobs (tasks) to a set of machines (resources) over time, subject to precedence and capacity constraints, with the goal of optimizing performance metrics such as makespan (total completion time), lateness, or cost. The job shop problem is a classic combinatorial optimization problem in operations research, addressed through heuristics (greedy dispatching rules, simulated annealing, genetic algorithms) and exact algorithms (branch-and-bound, constraint programming). It is fundamental to manufacturing, project management, and computational scheduling.

Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer.

Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285.

aggregate-planning material-requirements-planning assembly-line-balancing facility-layout scor-model


Johansen Cointegration Test

Johansen Cointegration Test and Vector Error Correction Model (VECM)

Johansen test
VECM
vector error correction model
multivariate cointegration
Johansen Eşbütünleşme Testi ve VECM

The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium.

Multivariate cointegration / vector error correction model

Maximum likelihood (reduced-rank regression)

Multiple I(1) (first-order integrated) time series

Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580.

Johansen, S. (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.

ardl-bounds-test var-model arima engle-granger-cointegration augmented-dickey-fuller


Joint Model for Longitudinal and Survival Data

Joint Model for Longitudinal and Time-to-Event Data

Joint Model (Boylamsal + Sağkalım Birleşik Model)

Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.

The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through shared random effects. It resolves two major problems that simpler approaches cannot handle: informative dropout from longitudinal studies and the endogeneity of time-varying biomarkers used as covariates in a Cox model.

Longitudinal (mixed-effects) + Survival (Cox or parametric)

Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.

Tsiatis, A.A. & Davidian, M. (2004). Joint Modeling of Longitudinal and Time-to-Event Data: An Overview. Statistica Sinica, 14(3), 809–834.

cox-ph kaplan-meier time-dependent-cox frailty-model landmark-analysis mixed-effects-model


Joint Reaction Force

Joint contact force
Tibiofemoral force
Joint loading

Joint reaction force (JRF) estimation calculates the contact forces transmitted across joints during movement using inverse dynamics combined with anatomical modeling. First validated in vivo by Bergmann and colleagues using instrumented hip implants, JRF estimation is essential for understanding joint degeneration, designing orthopedic implants, and assessing injury risk.

Bergmann, G., Deuretzbacher, G., Heller, M., Graichen, F., Rohlmann, A., Strauss, J., & Duda, G. N. (2001). Hip forces and gait patterns from routine activities. Journal of Biomechanics, 34(7), 859-871.

Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. Wiley-Interscience.


Jonckheere-Terpstra Test

Jonckheere-Terpstra Test for Ordered Alternatives

Jonckheere-Terpstra Testi
JT test
ordered k-sample test
trend test for ordered groups

The Jonckheere-Terpstra test is a nonparametric hypothesis test that detects a monotone trend across k ordered groups — testing whether the outcome rises (or falls) systematically as the group order increases. Developed independently by T. J. Terpstra (1952) and A. R. Jonckheere (1954), it is the directional, ordered-alternative counterpart to the Kruskal-Wallis test.

Jonckheere, A. R. (1954). A distribution-free k-sample test against ordered alternatives. Biometrika, 41(1-2), 133–145.

Terpstra, T. J. (1952). The asymptotic normality and consistency of Kendall's test against trend, when ties are present in one ranking. Indagationes Mathematicae, 14, 327–333.

Hollander, M., Wolfe, D. A., & Chicken, E. (2014). Nonparametric Statistical Methods (3rd ed.). Wiley.

kruskal-wallis mann-whitney-u spearman-correlation friedman-test one-way-anova


Jones Accrual Model

Jones Accrual Model for Detecting Earnings Management

The Jones Accrual Model, developed by Jennifer J. Jones in 1991, is a statistical method for detecting earnings management in financial statements by isolating abnormal accruals. It distinguishes between normal business accruals and potentially manipulated accruals, helping auditors and analysts identify potential financial statement fraud.

Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193-228.

Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70(2), 193-225.

audit-risk-model analytical-procedures-auditing internal-control-evaluation fraud-risk-assessment


Jones Calculus

Jones vector method
Jones matrix
polarization calculus

Jones calculus is a mathematical formalism for analyzing the propagation and manipulation of polarized light using vectors and matrices. Developed by Robert Clark Jones in 1941, it represents the electric field of a coherent optical beam as a two-component complex vector (Jones vector) and optical elements as matrices (Jones matrices), enabling elegant tracking of polarization through optical systems.

Jones, R. C. (1941). A new calculus for the treatment of optical systems: I. Description and discussion of the calculus. Journal of the Optical Society of America, 31(7), 488-493.

Born, M., & Wolf, E. (1980). Principles of Optics (6th ed.). Pergamon Press.

Goldstein, D. H. (2003). Polarized Light (2nd ed.). Marcel Dekker.


Journal Citation Reports

Institute for Scientific Information (ISI), now Clarivate Analytics

Journal Citation Reports (JCR) is an annual publication by Clarivate Analytics providing comprehensive citation metrics and performance analytics for journals indexed in Web of Science Core Collection. Launched in 1975, JCR publishes Impact Factor, the most widely recognized journal quality metric, alongside supplementary metrics (5-year IF, Journal Citation Indicator, Immediacy Index, Cited Half-Life, and citation distribution analysis). JCR is the authoritative source for journal ranking, benchmarking, and impact assessment in research evaluation systems globally. Access requires institutional subscription, though some institutions provide free access to affiliated researchers.

Clarivate Analytics. (2024). Journal Citation Reports. Retrieved from https://clarivate.com/webofsciencegroup/solutions/journal-citation-reports/

Clarivate Analytics. (2023). JCR Methodology Overview. https://clarivate.com/webofsciencegroup/essays/journal-citation-reports-methodology/

Garfield, E. (1979). Citation indexing: Its theory and applications in science, technology, and humanities. Philadelphia: ISI Press.

web-of-science impact-factor scimago-journal-rank scopus-database h-index


Journal Co-Citation Analysis

journal citation mapping
journal network analysis
cited source co-citation

Journal co-citation analysis is a bibliometric method that maps the intellectual structure of a research field by analyzing how frequently pairs of journals are cited together in the same papers. Two journals are co-cited when papers cite both journals, indicating that the journals are perceived as intellectually related by the citing authors. This extension of paper-level co-citation analysis to the journal level reveals the topological structure of journal relationships, disciplinary boundaries, and the role of different journals within research communities.

White, H. D., & Griffith, B. C. (1981). Author co-citation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171.

McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443.

co-citation-analysis science-mapping bibliographic-coupling keyword-co-occurrence


Journal Submission Process

Steps in Submitting a Manuscript to a Peer-Reviewed Journal

manuscript submission
journal submission
peer review process

Journal editors and publishing community; standards documented by ICMJE and COPE

Submitting a manuscript to a peer-reviewed journal is a multi-stage process: preparation, submission, editorial triage, peer review, revision, and publication. Understanding each stage helps authors avoid common pitfalls and set realistic expectations. Most journals use online submission systems (ScholarOne, Editorial Manager, OJS) that guide authors through the process. From submission to first editorial decision typically takes 30–90 days; acceptance to publication can take another 30–180 days depending on the journal's backlog and production timeline. Journals vary in acceptance rates (Nature ~5%, specialized journals 30–50%) and review times. Knowing the journal's policies and timelines before submitting prevents frustration.

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

Committee on Publication Ethics (2019). COPE Handbook on Publication Ethics. Retrieved from https://publicationethics.org/

Wager, E., & Wieland, B. (2011). Responsibilities of journal editors. Lancet, 337(9834), 1807–1809.

imrad-structure apa-style-guide vancouver-style revision-response-to-reviewers


Jump-Diffusion Model

Atlama Difüzyon Modeli (Merton Jump-Diffusion)

The Merton Jump-Diffusion model, introduced by Robert C. Merton in 1976, extends Geometric Brownian Motion by adding sudden price jumps generated by a Poisson process. It captures the volatility smile and the fat-tailed return behaviour that standard Black-Scholes cannot explain, and is widely used in option pricing and risk management.

Continuous-time asset price model (diffusion plus Poisson jumps)

Maximum likelihood or option-implied calibration

Merton, R. C. (1976). Option Pricing When Underlying Stock Returns Are Discontinuous. Journal of Financial Economics, 3(1–2), 125–144.

har-rv-model pairs-trading black-litterman-model garch-model geometric-brownian-motion


Just-About-Right Scaling

Just-About-Right (JAR) Scaling is a consumer-based sensory evaluation method that asks respondents to rate sensory attributes not on intensity alone, but on whether they perceive the attribute as too weak, just right, or too strong for the product. Developed by Lawless in the mid-1990s, JAR scaling bridges the gap between descriptive sensory analysis and consumer preference, directly linking attribute levels to consumer satisfaction.

Lawless, H. T. (1995). Evaluation of world wide web sites with sensory evaluation methods. Food Technology, 49(12), 90-92.

Meilgaard, M. C., Carr, B. T., & Civille, G. V. (2006). Sensory evaluation techniques (4th ed.). CRC Press.


JAFAR

Juvenile Arthritis Functional Assessment Report

Juvenile Arthritis Functional Assessment Scale

rheumatologic disease pediatric functional assessment

The JAFAR is a parent-report instrument developed by Lovell et al. in 1989 to assess functional ability in children and adolescents with juvenile idiopathic arthritis (JIA). Measuring across multiple domains including lower extremity function, upper extremity function, and activities of daily living, the JAFAR quantifies the extent to which arthritis and its treatment affect the child's mobility, self-care, and participation in age-appropriate activities. It remains a standard functional outcome measure in pediatric rheumatology research and clinical practice.

Parent report of child functional status

Lovell, D. J., Howe, S., Shear, E., Hartner, S., McGirr, G., Schulte, M., & Jaffe, R. (1989). Development of a disability measurement tool for juvenile rheumatoid arthritis. Arthritis & Rheumatism, 32(11), 1390-1395.

Lovell, D. J., Giannini, E. H., & Reiff, A. (1994). Etanercept in children with polyarticular juvenile rheumatoid arthritis. New England Journal of Medicine, 342(11), 763-769.

paqlq pedsql-diabetes child-health-questionnaire qolce


k-Anonymity

k-Anonymization
k-Anonymous Microdata
Quasi-Identifier Suppression Model
k-Anonimlik

k-Anonymity is a formal privacy model introduced by Latanya Sweeney in 2002 to protect individuals when personal data is released for research or public use. It requires that every record in a published dataset be indistinguishable from at least k−1 other records with respect to a designated set of quasi-identifying attributes — such as age, gender, and ZIP code — preventing re-identification by linking released data to external sources.

Input: Microdata table with quasi-identifiers

Output: Anonymized table where each record is indistinguishable from at least k-1 others

Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.


k-Core Decomposition

Core Decomposition
Coreness Decomposition
Shell Decomposition
Çekirdek Ayrıştırma

k-Core Decomposition is a graph-theoretic method that partitions the vertices of a network into a nested sequence of subgraphs called k-cores. A k-core is the maximal subgraph in which every vertex has at least k neighbors within that subgraph. Introduced by Stephen B. Seidman in 1983, the method assigns each vertex a coreness number that captures its structural centrality relative to the local connectivity of the graph.

Graph pruning and hierarchical decomposition

Output: Coreness number per vertex

Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.


K-Means Clustering

K-Means Clustering (Lloyd–MacQueen Algorithm)

K-Ortalamalar Kümeleme
k-ortalamalar kümeleme
k-means
centroid clustering

K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.

MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.

hierarchical-clustering lda-classification principal-component-analysis random-forest


K-means

k-means clustering
Lloyd's algorithm
k-means partitioning
hard k-means

K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.

Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.

MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.

gaussian-mixture-model dbscan hierarchical-clustering k-nearest-neighbors pca t-sne


Kessler Psychological Distress Scale

The Kessler Psychological Distress Scale-10 (K10) is a 10-item self-report measure of non-specific psychological distress and mental health problems. Developed by Kessler and colleagues in 2002, the K10 was designed as an ultra-brief screening instrument for population surveys and epidemiological research. A shorter 6-item version (K6) is also widely used for rapid case identification.

Non-specific psychological distress screening

Kessler, R. C., Andrews, G., Colpe, L. Y., Hiripi, E., Mroczek, D. K., Normand, S. L., ... & Zaslavsky, A. M. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32(6), 959-976.

Furukawa, T. A., Kessler, R. C., Slade, T., & Andrews, G. (2003). The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine, 33(2), 357-362.


Kalman Filter (Finance)

Kalman Filter — Financial State-Space Model

Kalman Filtresi — Finansal Durum Uzayı Modeli

Harvey (structural time series treatment); Kim & Nelson (state-space with regime switching)

The Kalman filter is a recursive algorithm that estimates financial models with time-varying parameters, hidden factors, and noisy observations inside a dynamic state-space framework. The structural time series treatment was set out by Harvey (1989), with state-space and regime-switching extensions developed by Kim and Nelson (1999); it is widely applied to pairs trading, time-varying beta estimation, and yield-curve modelling.

Recursive predict-update filter; maximum likelihood / EM for parameters

Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.

Kim, C. J. & Nelson, C. R. (1999). State-Space Models with Regime Switching. MIT Press.

arima factor-risk-model stochastic-volatility-model principal-component-risk long-memory-models


Kalman Filter for Signal Tracking

Kalman Filter for Signal Estimation and Tracking

Kalman Filtering
Recursive State Estimation
Optimal Filtering

The Kalman filter is a recursive algorithm that optimally estimates the state of a linear dynamic system from noisy measurements, minimizing mean-square error. Introduced by Rudolf Kalman in 1960, it revolutionized control theory, navigation, and signal processing by enabling real-time optimal estimation for time-varying systems. The Kalman filter became indispensable for spacecraft tracking, GPS navigation, and countless modern applications.

Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35–45.

Grewal, M. S., & Andrews, A. P. (2015). Kalman Filtering: Theory and Practice with MATLAB (4th ed.). Wiley-IEEE Press.

wiener-filter adaptive-lms-filter matched-filter fir-filter-design


Kalman Filter with Measurement Error

Kalman Filter with Explicit Measurement Error Modeling

state-space model with observation error

linear quadratic estimation with observation noise

The Kalman filter with measurement error is a recursive Bayesian state-space algorithm that estimates the true hidden state of a dynamic system from noisy observations. It explicitly separates process noise (system dynamics uncertainty) from measurement noise (observation uncertainty), propagating both sources of error through a two-step predict-update cycle to yield optimal filtered state estimates and their associated uncertainty.

Recursive Bayesian state-space estimator

Sequential / time-series observations with additive Gaussian noise

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.

Durbin, J. & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press.


Kalman Filter with Missing Data

Kalman Filter for State-Space Models with Missing Observations

state-space model with missing observations

Rudolf E. Kálmán (Kalman filter, 1960); missing-data extension formalised by Shumway & Stoffer (1982) via EM

The Kalman filter with missing data extends the classical Kalman filter to handle time series in which some observations are absent. When an observation is missing at time t the update step is skipped and the state estimate is carried forward from the prediction step alone. Combined with the Expectation-Maximisation (EM) algorithm, the approach also estimates unknown model parameters from incomplete data, making it a practical tool for real-world irregularly observed series.

Sequential Bayesian filter / state-space smoother

Multivariate time series with intermittent missing observations

Shumway, R. H. & Stoffer, D. S. (2000). Time Series Analysis and Its Applications. Springer.

kalman-filter particle-filter-with-missing-data sequential-monte-carlo bayesian-inference-with-missing-data state-space-model em-algorithm


Kalman Filter

Kalman Filter (Linear-Gaussian State-Space Filter)

linear quadratic estimator
LQE
Kalman-Bucy filter
optimal recursive filter

The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.

sequential / time-series, continuous observations

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.

Welch, G. & Bishop, G. (2006). An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, Technical Report TR 95-041.

sequential-monte-carlo particle-filter bayesian-regression hidden-markov-model extended-kalman-filter dynamic-bayesian-network


Kanban

Kanban is a pull-based production control system developed by Taiichi Ohno at Toyota in the 1950s that uses visual signals (traditionally cards or bins) to trigger production and movement of materials based on actual demand rather than forecasts. The Japanese word 'kanban' means 'visual card' or 'sign,' and the system operates on the principle that work should flow in response to downstream requirements. Kanban is a foundational element of the Toyota Production System and lean manufacturing, enabling just-in-time production, reduced inventory, and improved flow efficiency.

Ohno, T. (1988). Toyota production system: Beyond large-scale production. Cambridge, MA: Productivity Press.

Rother, M., & Shook, J. (2003). Learning to see: Value stream mapping to add value and eliminate muda. Cambridge, MA: Lean Enterprise Institute.

material-requirements-planning aggregate-planning vendor-managed-inventory smed scor-model


Kano Model

Kano Analysis
Attractive-Performance-Basic Model

The Kano Model is a framework for categorizing product or service features based on their impact on customer satisfaction. Developed by Noriaki Kano, this model distinguishes three types of features: basic (must-have) features that satisfy minimally but cause significant dissatisfaction if absent; performance features that increase satisfaction proportionally with their level; and attractive (delightful) features that exceed expectations and generate disproportionate satisfaction. By classifying features using the Kano Model, product teams prioritize development efforts, balance risk and innovation, and design experiences that delight rather than merely satisfy.

Two-dimensional model categorizing product/service features by satisfaction impact

Kano, N., Seraku, N., Takahashi, F., & Tsjui, S. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control, 14(2), 147–156.

Cohen, L. (2007). Quality function deployment and six sigma. Pearson Education.

system-usability-scale nasa-tlx attrakdiff-ueq user-research


cardiomyopathy and heart failure quality of life

The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a 23-item, multidimensional self-report measure that evaluates heart failure-related symptoms, functional limitations, and quality of life in patients with cardiomyopathy and heart failure of all severities. Developed by Spertus and colleagues in 2000, the KCCQ provides six disease-specific domains and a clinically summary score, making it ideal for comprehensive, domain-focused assessment in both clinical practice and research.

Green, C. P., Porter, C. B., Bresnahan, D. R., & Spertus, J. A. (2000). Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health-related quality of life measure for heart failure. Journal of the American College of Cardiology, 35(5), 1245–1253.

minnesota-heart-failure seattle-angina-questionnaire new-york-heart-association-class duke-activity-status-index


Kaplan-Meier Analysis

KM analysis
KM estimator
product-limit estimator
Kaplan-Meier curve

Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research.

Time-to-event data with censoring (survival times, event indicators)

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.

survival-analysis cox-proportional-hazards competing-risks-analysis log-rank-test cohort-study randomized-clinical-trial


Kaplan-Meier Estimator

Kaplan-Meier Survival Function Estimator

KM estimator
product-limit estimator
Kaplan-Meier curve
survival curve estimator

The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.

Output: Step-function survival curve S(t)

Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.

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

log-rank-test cox-proportional-hazards nelson-aalen-estimator accelerated-failure-time-model competing-risks-analysis


Kaplan-Meier

product-limit estimator
km curve
kaplan-meier sağkalım analizi

The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.

Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.

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

cox-ph nelson-aalen log-rank-test fine-gray-model


Karl Fischer Titration

Karl Fischer Titration (KFT) is a precise analytical method for determining water content in food and pharmaceutical products. Developed by Karl Fischer in 1935, KFT uses a chemical reaction between water and an iodine-based titrant, allowing quantification of moisture with exceptional accuracy and sensitivity. KFT is the official gold-standard method for water determination in numerous food and pharmaceutical standards worldwide.

Karl Fischer. Neue Methode zur Maßstabbestimmung des Wassers in Flüssigkeiten und Gasen. Angewandte Chemie, 48(44), 394-396. (1935)

Scholz, E. (2004). Karl Fischer titration (2nd ed.). Springer-Verlag.


Katz Index of Independence in ADL

Katz Index of Independence in Activities of Daily Living (ADL)

The Katz Index of Independence in Activities of Daily Living, developed by Sidney Katz and colleagues in 1963, is one of the earliest and most widely used tools for assessing functional status in older adults and persons with chronic illness. The scale evaluates six essential self-care activities (bathing, dressing, toileting, transfer, continence, feeding) through direct observation or interview and assigns an overall grade (A through G) reflecting the degree of independence. It remains a foundational instrument in geriatric assessment, rehabilitation medicine, and long-term care settings.

Clinician-rated or observational functional assessment

Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., & Jaffe, M. W. (1963). Studies of Illness in the Aged: The Index of ADL, a standardized measure of biological and psychosocial function. JAMA, 185(12), 914-919.

Katz, S., Downs, T. D., Cash, H. R., & Grotz, R. C. (1970). Progress in development of the Index of ADL. Gerontologist, 10(1), 20-30.


Keetch-Byram Drought Index

The Keetch-Byram Drought Index (KBDI) is a cumulative drought severity index used in fire danger rating systems to track long-term soil moisture depletion and drying trends. Developed in 1968 by Keetch and Byram, KBDI integrates daily temperature, precipitation, and prior drought state to produce a continuous index ranging from 0 (no drought, moist soil) to 800 (severe drought, very dry soil). KBDI is widely used in fire danger prediction and fire behavior modeling because soil moisture is a major driver of fuel drying and flammability.

Keetch, J. J., & Byram, G. M. (1968). A drought index for forest fire control. Research Paper SE-38, USDA Forest Service Southeastern Forest Experiment Station.

Burgan, R. E. (1988). 1988 revisions to the 1978 National Fire-Danger Rating System. Research Paper SE-273.


Kelly Criterion

Kelly Criterion for Optimal Position Sizing

The Kelly Criterion (1956) is a formula for optimal bet sizing that maximizes the long-run logarithmic growth of wealth. It specifies the optimal fraction of capital to risk on each trade based on win probability and payoff ratio. The criterion has become foundational in quantitative trading, portfolio management, and behavioral economics.

Kelly, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.

Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.


KEMENY-YOUNG

KEMENY-YOUNG (Kemeny-Young — Optimal rank aggregation minimising Kendall τ disagreement) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Kemeny, J. G. in 1959. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Rank aggregation (Kemeny consensus, NP-hard optimisation)

Kemeny, J. G. (1959). Mathematics without numbers. Daedalus


KEMIRA

KEMIRA (KEmeny Median Indicator Ranks Accordance) is a ranking multi-criteria decision-making (MCDM) method introduced by Krylovas, A. Zavadskas, E. K. Kosareva, N. Dadelo, S. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Group-of-criteria Kemeny-median weight elicitation + additive aggregation across two attribute groups

Krylovas, A., Zavadskas, E. K., Kosareva, N., Dadelo, S. (2014). New KEMIRA method for determining priorities of the attributes in solving MCDM problem. International Journal of Information Technology & Decision Making


Kendall Tau Correlation

Kendall Tau Rank Correlation Coefficient

Kendall's tau
Kendall tau-b
tau correlation
Kendall Tau Korelasyonu

Kendall Tau is a nonparametric rank correlation coefficient introduced by Maurice G. Kendall in 1938 to measure the strength and direction of a monotone association between two ordinal or continuous variables. It is particularly suited to small samples and datasets containing many tied ranks, where the Spearman coefficient can be less stable.

Asymptotic normal (large n); exact permutation (small n)

Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), 81–93.

spearman-correlation pearson-correlation mann-whitney-u wilcoxon-signed-rank


Kendall's tau

Kendall's Tau Rank Correlation Coefficient

Kendall tau
Kendall rank correlation
tau-b
tau-c

Kendall's tau is a nonparametric measure of the ordinal association between two variables. It quantifies how consistently the relative ordering of one variable matches the ordering of another across all observation pairs, making it robust to outliers and suitable for ordinal or non-normally distributed data.

Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1/2), 81–93.

Kendall, M. G. (1948). Rank Correlation Methods. Charles Griffin & Company.

spearman-correlation pearson-correlation mann-whitney-u-test wilcoxon-signed-rank-test roc-analysis effect-size-analysis


Ruth A. Baer, Greg T. Smith, and Kristin B. Allen

The Kentucky Inventory of Mindfulness Skills (KIMS) is a 39-item self-report questionnaire measuring trait mindfulness across four theoretically distinct skills: Observing, Describing, Acting with Awareness, and Accepting Without Judgment. Developed by Baer, Smith, and Allen in 2004 at the University of Kentucky, the KIMS was one of the first multidimensional mindfulness measures and served as a foundational model for subsequent instruments including the widely used Five Facet Mindfulness Questionnaire (FFMQ). The KIMS remains a valuable tool for research and clinical assessment, particularly in settings emphasizing skill-based approaches to mindfulness development.

Baer, R. A., Smith, G. T., & Allen, K. B. (2004). Assessment of mindfulness by self-report: The Kentucky Inventory of Mindfulness Skills (KIMS). Assessment, 11(3), 191-206.


Kernel Density Estimation

Kernel Density Estimation and Distribution Testing (KDE)

Çekirdek Yoğunluk Tahmini ve Testi (KDE)

Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman

Kernel Density Estimation is a nonparametric method that estimates a continuous probability density by placing a smooth kernel function over each observation, without assuming any parametric distribution. It traces back to Rosenblatt (1956) and the textbook treatment by Silverman (1986), and it also supports distribution-comparison tests built on the estimated densities.

Kernel-smoothed empirical density (Parzen-Rosenblatt estimator)

Silverman rule, Sheather-Jones, or cross-validation

Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837.

Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall / CRC Press.

histogram anderson-darling-test lilliefors-test mood-median-test quantile-regression


Kernel PCA

KPCA
kernel PCA
nonlinear PCA via kernel trick
kernel eigenvalue decomposition

Schölkopf, B.; Smola, A. J.; Müller, K.-R.

Kernel Principal Component Analysis (Kernel PCA) is a nonlinear dimensionality-reduction method introduced by Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller in 1997–1998. It extends classical linear PCA to curved, non-linear data manifolds by implicitly mapping input data into a high-dimensional feature space via a kernel function, then performing standard PCA in that space — all without ever computing the mapping explicitly.

Nonlinear dimensionality reduction via kernel trick

Unsupervised feature extraction / manifold learning

O(n^3) eigendecomposition of the n × n kernel (Gram) matrix

Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.

Schölkopf, B., Smola, A. J., & Müller, K.-R. (1997). Kernel principal component analysis. In Artificial Neural Networks — ICANN'97, Lecture Notes in Computer Science, Vol. 1327, pp. 583–588. Springer.

Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.

principal-component-analysis svm-classification t-sne umap autoencoder isomap locally-linear-embedding


Musical Key Detection

Musical Key Detection and Estimation Algorithm

key recognition
tonality estimation
musical center detection

Musical key detection is the task of automatically determining the key (tonal center) and scale mode of a musical composition from its audio. Introduced formally by Gómez (2006), it is essential for music analysis, transposition, harmonic understanding, and music theory education. The key defines the tonal center around which a piece gravitates; identifying it enables deeper structural understanding. Key detection is closely related to chord recognition but operates at a higher level of abstraction.

Gómez, E. (2006). Tonal description of polyphonic audio for music content processing. In INESC Porto PhD Thesis.

Noland, K., & Sandler, M. (2007). Signal processing parameters for tonality estimation. In Proceedings of the International Symposium on Music Information Retrieval.

Khadkevich, M., & Omologo, M. (2011). Community structure in networks of classical music composers. Journal of Cultural Economics, 35(4), 307-319.

chord-recognition harmonic-analysis-music pitch-detection-algorithm beat-tracking music-genre-classification


Keyword Co-Occurrence Analysis

term co-occurrence
keyword network analysis
thematic analysis
term clustering

Keyword co-occurrence analysis is a text mining and bibliometric method that identifies research themes and their relationships by analyzing how frequently terms or keywords appear together in abstracts, titles, or indexed keywords of scientific publications. When two keywords appear together frequently, they are considered co-occurring, indicating a shared thematic or conceptual relationship. This method rapidly reveals the topical structure of a research field without relying on formal classifications, making it particularly useful for detecting emerging research areas and understanding disciplinary boundaries.

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166.

Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

science-mapping bibliographic-coupling co-citation-analysis vosviewer-citespace


Keyword Extraction

Anahtar Kelime Çıkarma (Keyword Extraction)

Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).

Statistical (frequency / TF-IDF) / graph-based (TextRank) / embedding-based (KeyBERT)

Output: Ranked list of keywords or keyphrases per document

Reliable statistical extraction needs a sizeable corpus

Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.

Grootendorst, M. (2020). KeyBERT. Zenodo.

topic-modeling-nmf tf-idf sentiment-analysis readability-analysis


KDQOL

The KDQOL is the most widely used quality of life measure for chronic kidney disease (CKD) patients, particularly those on dialysis. Developed by Ron Hays and colleagues in 1994, this multidimensional questionnaire (full version 134 items; short-form KDQOL-SF 36 items) measures kidney disease-specific impacts on physical function, emotional well-being, social participation, and treatment burden. It is the standard outcome measure in renal research and clinical practice.

Self-report quality of life questionnaire

Hays, R. D., Kallich, J. D., Mapes, D. L., Coons, S. J., & Carter, W. B. (1994). Development of the Kidney Disease Quality of Life (KDQOL) instrument. Quality of Life Research, 3(5), 329-338.

Ware Jr, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220-233.

Unruh, M. L., Benz, R., Greene, T., Has, P., Miskulin, D., Yan, G., ... & HEMO Study Group. (2004). Effects of hemodialysis dose and membrane flux on health-related quality of life in the HEMO Study. American Journal of Kidney Diseases, 44(5), 832-843.


Kinematic Distance

Galactic Kinematic Distances
Rotation-Curve Distance
Kinematic Parallax

Kinematic distance is a method for estimating distances to objects in the Milky Way using their observed radial velocities and the known rotation curve of the Galaxy. Developed in the 1950s by Bert Westerhout and others, this technique enables distance determination to distant molecular clouds and masers without trigonometric parallax or individual object luminosities.

Reid, M. J., et al. (2014). Trigonometric parallaxes of high mass star forming regions: the structure and kinematics of the Milky Way. Astrophysical Journal, 783(2), 130.

Brand, J., & Blitz, L. (1993). The latitude-velocity distribution of molecular clouds: evidence for a new Galactic component. Astronomy & Astrophysics, 275, 67-87.

Green, G. M., et al. (2019). A 3D Dust Map Based on Gaia, Pan-STARRS 1 and 2MASS. Astrophysical Journal, 887(2), 93.


Kjeldahl Method

The Kjeldahl Method is a classical analytical procedure for determining the total nitrogen content of food products, developed by Johan Kjeldahl in 1883. By measuring total nitrogen and applying a conversion factor specific to the food type, the method indirectly determines crude protein content. Kjeldahl remains the official standard method for protein determination in foods, nutraceuticals, and animal feeds worldwide.

Kjeldahl, J. G. C. T. (1883). Neue Methode zur Bestimmung des Stickstoffs in organischen Körpern. Zeitschrift für Analytische Chemie, 22, 366-383.

AOAC International (2016). Official Methods of Analysis (20th ed.). AOAC International.


KKR Method

The Korringa-Kohn-Rostoker (KKR) method is a powerful multiple-scattering approach for calculating electronic band structures and properties of periodic and disordered solids. Developed in the late 1940s, KKR treats electrons as scattering from atomic potentials in a muffin-tin geometry, enabling efficient calculations for both crystalline and amorphous systems.

Korringa, J. (1947). On the calculation of the energy of a Bloch wave in a metal. Physica, 13, 392–400.

Gyorffy, B. L. (1972). Coherent potential approximation for random substitutional binary alloys. Physical Review B, 5, 2382–2384.

Vosko, S. H., Wilk, L., Nusair, M. (2003). Accurate spin-dependent electron liquid correlation energies. Canadian Journal of Physics, 58, 1200–1211.


Klason Lignin

The Klason lignin method is a standard chemical test for quantifying the acid-insoluble lignin content in wood and plant biomass. Developed by Erik Klason in 1908, the method treats wood with sulfuric acid to dissolve carbohydrates (cellulose and hemicellulose) while leaving the acid-insoluble lignin residue. Klason lignin is widely used in wood science, pulp chemistry, and biomass characterization to assess wood composition and predict properties.

TAPPI T222 om-15. (2015). Acid-insoluble lignin in wood and pulp. TAPPI Press.

Sluiter, A., Hames, B., Ruiz, R., et al. (2008). Determination of structural carbohydrates and lignin in biomass. Technical Report NREL/TP-510-42618.


KLM-GOMS

Keystroke-Level Model - Goals, Operators, Methods, Selection Rules

The Keystroke-Level Model (KLM), part of the Goals-Operators-Methods-Selection rules (GOMS) framework, is a computational method for predicting how long a user will take to accomplish a routine task using an interactive system. Developed by Card, Moran, and Newell in 1983, KLM decomposes user actions into primitive operators (keystrokes, mouse clicks, mental preparation, system response waits) with empirically derived execution times, enabling designers to estimate task performance without running user studies.

Computational cognitive model for task execution time prediction

Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates.

Kieras, D. E. (1997). A Guide to GOMS Task Analysis. Technical Report. University of Michigan.

heuristic-evaluation cognitive-walkthrough think-aloud-protocol system-usability-scale


K-Nearest Neighbors

K-Nearest Neighbors (KNN) Classification and Regression

KNN
K-En Yakın Komşu (KNN)
nearest neighbor classifier
instance-based learning

K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.

Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.

naive-bayes logistic-regression svm-classification random-forest decision-tree


Knowledge Distillation

Knowledge Distillation (Teacher–Student Model Compression)

Bilgi Damıtma (Knowledge Distillation)
bilgi damıtma
teacher-student distillation
model distillation

Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster.

Neural network compression (teacher–student)

Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop.

Sanh, V., Debut, L., Chaumond, J. & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108.

mixture-of-experts longformer-bigbird contrastive-learning-dl xgboost random-forest


Knowledge Graph Analysis

Knowledge Graph Analysis (Semantic Network Representation and Reasoning)

KG analysis
semantic graph analysis
knowledge base graph analysis
entity-relation graph analysis

Ehrlinger, L. & Wöß, W.; Google (popularized)

Knowledge Graph Analysis is a framework for representing, storing, and reasoning over structured factual knowledge as a directed graph of entities and typed relations. Entities (nodes) and relationships (edges) are expressed as subject–predicate–object triples, enabling rich querying, inference, and integration of heterogeneous data sources across domains such as biomedical research, e-commerce, and scientific literature.

Graph-based knowledge representation and analysis

Entity-relation triples (RDF/property graphs), ontologies, linked data

Ehrlinger, L. & Wöß, W. (2016). Towards a Definition of Knowledge Graphs. In Proceedings of the SEMANTICS Posters and Demos Track (SEMANTiCS 2016). CEUR Workshop Proceedings, vol. 1695.

Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. de, Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 71.

social-network-analysis modularity-analysis exponential-random-graph-model network-diffusion-analysis two-mode-network-analysis multiplex-network-analysis


Knowledge Graph Embeddings

Knowledge Graph Embeddings (TransE and beyond)

KG Embeddings
Knowledge Graph Representation Learning
Relational Embeddings
Bilgi Grafı Gömme

Bordes, Usunier, García-Durán, Weston & Yakhnenko

Knowledge Graph Embeddings (KGE) are a family of methods that represent entities and relations in a knowledge graph as dense, low-dimensional vectors in a continuous space. The foundational model, TransE, was introduced by Bordes, Usunier, García-Durán, Weston, and Yakhnenko in 2013. TransE treats each relation as a translation in embedding space — the head entity vector plus the relation vector should approximate the tail entity vector for any true triple (h, r, t). This simple geometric principle enabled effective link prediction and knowledge base completion at scale.

Graph representation learning via low-dimensional vector embeddings

O((|E| + |R|) × d) where d is embedding dimension

Bordes, A., Usunier, N., García-Durán, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 26.


Knowledge Graph Construction

Bilgi Grafiği Oluşturma (Knowledge Graph)

Knowledge graph construction is a text-mining pipeline that turns unstructured text into a structured graph of entities and the relations between them. Drawing on the synthesis of Hogan et al. (2021) and the relational-machine-learning review of Nickel et al. (2016), it represents knowledge as nodes (entities such as people, places, organisations) connected by labelled edges (relations), and serves semantic search, recommendation systems, and reasoning.

Structured knowledge representation pipeline

Input: Unstructured text

Output: Graph of entities (nodes) and relations (edges)

Named-entity recognition, relation extraction, entity linking

Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37.

Nickel, M. et al. (2016). A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE, 104(1), 11-33.


Knowledge Management Capability Scale

Knowledge Management (KM) Organizational Capability Assessment Scale

KM Capability Scale
Knowledge Management Maturity Scale

Ikujiro Nonaka and Hirotaka Takeuchi (SECI model); adapted by organizational scholars

Knowledge Management (KM) refers to the organizational capacity to create, capture, organize, and apply knowledge to improve organizational effectiveness, innovation, and decision-making. Nonaka and Takeuchi's (1995) knowledge-creating company framework conceptualized knowledge as moving through four conversion modes: socialization (tacit to tacit knowledge transfer through experience), externalization (tacit knowledge articulation into explicit forms), combination (explicit knowledge assembly into systems), and internalization (explicit knowledge absorption into tacit understanding). This scale measures organizational capability across the four KM processes—knowledge creation, capture, sharing, and application—revealing where organizations excel or struggle in converting information into competitive advantage.

Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.

Choo, C. W. (2002). Information management for the intelligent organization: The art of scanning the environment. ASIST Monograph Series.

Davenport, T. H., & Prusak, L. (2000). Working knowledge: How organizations manage what they know. Harvard Business School Press.

absorptive-capacity-scale dynamic-capabilities-scale supply-chain-integration-scale organizational-resilience-scale innovation-ambidexterity-scale


Knowledge Sharing Scale

Organizational Knowledge Sharing Intention

Bock, Zmud, Kim, and Lee; refined by Hau et al.

The Knowledge Sharing Scale (KSS) is an 18-item instrument measuring employee intention to share knowledge and experience within organizations. Developed by Bock, Zmud, Kim, and Lee in 2005, the KSS assesses barriers and enablers of knowledge sharing behavior across six dimensions: perceived usefulness, extrinsic motivation, intrinsic motivation, social norms, organizational climate, and knowledge-sharing intention.

Hau, Y. S., Kim, B., Lee, H., & Kim, Y. G. (2013). The effects of individual motivations and social capital on employees' tacit and explicit knowledge sharing intentions. International Journal of Information Management, 33(3), 356-366.

Bock, G. W., Zmud, R. W., Kim, Y. G., & Lee, J. N. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87-111.


Knowledge Space Theory

KST
Knowledge Structures
Competence-Based Knowledge Space Theory
Bilgi Uzayı Teorisi

Jean-Paul Doignon & Jean-Claude Falmagne

Knowledge Space Theory (KST) is a combinatorial, set-theoretic framework for modeling and assessing human knowledge, introduced by Jean-Paul Doignon and Jean-Claude Falmagne in 1985. It represents a learner's competence as a subset of a problem domain, organizes all feasible competence subsets into a lattice called a knowledge space, and uses probabilistic inference to locate a learner within that space. The approach underlies adaptive testing and intelligent tutoring systems, offering a mathematically rigorous alternative to classical test theory.

Combinatorial knowledge assessment framework

Doignon, J.-P., & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23(2), 175–196.


KTA

KTA
Knowledge-to-Action
KTA Framework
Knowledge-to-Action Cycle

Ian D. Graham, PhD; Roberta L. Logan, MD, MSc; colleagues at Ottawa Hospital Research Institute

The Knowledge-to-Action (KTA) Framework is a conceptual model and process guide for translating evidence into practice, developed by Ian Graham and colleagues at the Ottawa Hospital Research Institute (2004–2006). The KTA framework addresses a central challenge in implementation science: research evidence alone does not change practice; a deliberate, systematic process is required to adapt evidence to local contexts, identify and overcome implementation barriers, and sustain change. The KTA distinguishes between knowledge production (research, evidence synthesis) and knowledge application (implementation planning, barrier identification, strategy selection, execution, monitoring, and adaptation). The framework has become one of the most widely adopted implementation models in healthcare, particularly in Canada and internationally, and provides a structured approach to evidence-based practice implementation that is context-sensitive and iterative.

Graham, I. D., & Logan, R. L. (2004). Translating research into practice: A perspective on technology transfer. Journal of the American Medical Informatics Association, 11(2), 141–145.

Graham, I. D., Logan, R. L., Harrison, M. B., Straus, S. E., Tetroe, J., Caswell, W., & Robinson, N. (2006). Lost in knowledge translation: Time for a map? Journal of Continuing Education in the Health Professions, 26(1), 13–24.


Knowledge Tracing

BKT
Bayesian Knowledge Tracing
Deep Knowledge Tracing
Bilgi İzleme

Knowledge Tracing (KT) is a student-modeling technique that estimates, at each moment in time, the probability that a learner has mastered a target knowledge component. Introduced by Corbett and Anderson in 1994, the classical Bayesian Knowledge Tracing (BKT) model treats skill acquisition as a two-state Hidden Markov Model driven by four interpretable parameters: prior knowledge, learning rate, slip, and guess. Deep variants (DKT, DKVMN, AKT) later replaced HMMs with recurrent and transformer architectures.

Hidden Markov Model (classical); Recurrent Neural Network (deep variant)

Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.


Knowledge Translation

Knowledge Translation: Integration of Research Evidence into Clinical and Policy Practice

Canadian Institutes of Health Research (CIHR)

Knowledge Translation (KT) is the systematic synthesis, dissemination, exchange, and application of research findings to improve health outcomes and healthcare practice. First formalized by the Canadian Institutes of Health Research in 2004, KT recognizes that evidence generation alone does not automatically change clinical or policy behaviour, and structures a purposeful process to bridge the gap between research and practice.

Canadian Institutes of Health Research. (2004). Knowledge Translation Strategy 2004-2009. CIHR, Ottawa.

Straus, S. E., Tetroe, J., & Graham, I. D. (2009). Defining knowledge translation. Canadian Medical Association Journal, 181(3-4), 165-166.

Graham, I. D., Logan, R. F., Harrison, M. B., Straus, S. E., Tetroe, J., Caswell, W., & Robinson, N. (2006). Lost in knowledge translation: Time for a map? Journal of Continuing Education in the Health Professions, 26(1), 13-24.

cfir-framework re-aim-framework implementation-outcome-taxonomy behavior-change-wheel normalization-process-theory


Kohler Theory

Köhler Equilibrium Theory for Cloud Droplet Formation

Kohler theory
Kohler equilibrium
Cloud droplet nucleation

Köhler theory is a foundational framework in cloud microphysics that predicts the equilibrium supersaturation required for an aerosol particle of given size and composition to grow into a cloud droplet. Published in 1936 by Hilding Köhler, it combines the Kelvin effect (vapor pressure enhancement over curved surfaces) with the Raoult effect (vapor pressure depression from dissolved solute) to explain cloud droplet formation.

Köhler, H. (1936). The nucleus in and the growth of hygroscopic droplets. Transactions of the Faraday Society, 32, 1152-1161.

Pruppacher, H. R., & Klett, J. D. (1997). Microphysics of Clouds and Precipitation (2nd ed.). Kluwer Academic Publishers.


Kolb Learning Style Inventory

LSI
Experiential Learning Style Inventory

The Kolb Learning Style Inventory (LSI) is a self-report assessment based on experiential learning theory that identifies how individuals prefer to learn. Developed by David Kolb in 1984, it classifies learners into four styles—Diverging, Assimilating, Converging, and Accommodating—based on two dimensions: how information is perceived and how it is processed.

Self-assessment learning style inventory

Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall.

Kolb, D. A., & Kolb, A. Y. (1999). The Learning Way: Learning Outcomes from a New Approach to Student Experience. Journal of Advanced Education, 24(3), 277-295.

study-process-questionnaire academic-self-efficacy-scale student-engagement-scale course-experience-questionnaire


Kolmogorov-Arnold Networks

Deep Learning, Neural Network Architectures, Approximation Theory

Kolmogorov-Arnold Networks (KAN) is a neural network architecture introduced by Liu et al. in 2024 that replaces linear transformations with learned univariate functions on edges. Inspired by the Kolmogorov-Arnold representation theorem, KAN achieves superior function approximation with fewer parameters than traditional MLPs, offering potential efficiency gains and improved interpretability.

Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., & Tegmark, M. (2024). KAN: Kolmogorov-Arnold Networks. arXiv preprint arXiv:2404.19756.

vision-transformer mamba neural-radiance-fields masked-autoencoders


Two-Sample Kolmogorov-Smirnov Test

KS two-sample test
two-sample KS test
İki Örneklem Kolmogorov-Smirnov Testi

The two-sample Kolmogorov-Smirnov test is a nonparametric procedure that asks whether two independent groups are drawn from the same continuous distribution. Building on Smirnov's 1948 tables, it compares the empirical cumulative distribution functions (CDFs) of the two samples and uses their maximum absolute distance as the test statistic.

Nonparametric two-sample distribution test

D = sup|F₁(x) − F₂(x)| (maximum distance between empirical CDFs)

Smirnov, N. V. (1948). Table for Estimating the Goodness of Fit of Empirical Distributions. Annals of Mathematical Statistics, 19(2), 279-281.

Conover, W. J. (1999). Practical Nonparametric Statistics (3rd ed.). Wiley.

mann-whitney-u anderson-darling wilcoxon-rank-sum permutation-test levene-brown-forsythe


Kolmogorov-Smirnov Test

KS test
K-S test
one-sample KS test
Kolmogorov-Smirnov Testi

Andrey Nikolaevich Kolmogorov; Nikolai Vasilyevich Smirnov

The Kolmogorov-Smirnov (KS) test is a nonparametric goodness-of-fit test that assesses whether a sample comes from a specified theoretical distribution, such as the normal or exponential. First formalised by Andrey Kolmogorov in 1933 and further developed by Nikolai Smirnov in 1948, it compares the empirical cumulative distribution function of the observed data against a target theoretical CDF and quantifies their maximum absolute deviation.

D (maximum absolute deviation between empirical and theoretical CDFs)

Kolmogorov, A. N. (1933). Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari, 4, 83–91.

Smirnov, N. V. (1948). Table for estimating the goodness of fit of empirical distributions. Annals of Mathematical Statistics, 19(2), 279–281.

Massey, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78.

shapiro-wilk anderson-darling lilliefors-test kolmogorov-smirnov-2sample chi-square-goodness-of-fit


Kónya Bootstrap Causality

Bootstrap Panel Causality Test
Kónya Panel Granger Causality
SUR-Based Bootstrap Causality
Kónya Önyükleme Nedensellik Testi

Introduced by László Kónya in 2006, this method tests Granger causality in heterogeneous panels by estimating a Seemingly Unrelated Regressions (SUR) system and deriving country-specific critical values through bootstrapping. Unlike pooled panel tests, it delivers a separate causality verdict for each cross-section, making it particularly valuable in applied macroeconomics and international economics when panel units are expected to behave differently.

Non-parametric bootstrap hypothesis test

Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992.


Koopa

Koopa (Koopman Predictors for Non-stationary Dynamics)

Koopman Predictor
Koopman-based Time-Series Model
Koopa Forecaster
Koopman Tahmincisi

Koopa is a deep learning model for time-series forecasting introduced by Yong Liu, Chang Li, Jianmin Wang, and Mingsheng Long at NeurIPS 2023. It addresses the challenge of non-stationarity by disentangling time series into stationary and non-stationary components, then modeling the non-stationary dynamics using a learned approximation of the Koopman operator — a mathematical framework that lifts nonlinear systems into a linear space for tractable long-horizon prediction.

Koopman operator-based time-series forecasting model

Liu, Y., Li, C., Wang, J., & Long, M. (2023). Koopa: Learning non-stationary time series dynamics with Koopman predictors. NeurIPS.


The Knee Injury and Osteoarthritis Outcome Score (KOOS) is a patient-reported outcome measure designed for active patients with knee injury and osteoarthritis. Developed by Roos and colleagues in 1998, KOOS extends assessment beyond traditional osteoarthritis scales to include symptoms, pain, function in daily living, function in sport and recreation, and knee-related quality of life—making it ideal for younger, more active populations.

Roos, E. M., Roos, B. P., Lohmander, L. S., Ekdahl, C., & Beynnon, B. D. (1998). Knee Injury and Osteoarthritis Outcome Score (KOOS): development of a self-administered outcome measure. Journal of Orthopaedic & Sports Physical Therapy, 28(2), 88–96.

Roos, E. M., & Toksvig-Larsen, S. (2003). Knee injury and Osteoarthritis Outcome Score (KOOS): validation and comparison in total meniscectomy versus meniscus preservation surgery for degenerative meniscus tears. Osteoarthritis and Cartilage, 11(5), 330–337.


KPSS Test

Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Stationarity Test

Kwiatkowski-Phillips-Schmidt-Shin test
stationarity test
KPSS durağanlık testi

The KPSS test, introduced by Kwiatkowski, Phillips, Schmidt and Shin in 1992, tests the null hypothesis that a series is stationary against the alternative that it contains a unit root — the reverse of the ADF and Phillips-Perron tests. By flipping the burden of proof, it is designed to be used alongside unit-root tests so that the two can confirm one another and expose ambiguous, borderline cases.

Stationarity test (reverse of unit-root tests)

Non-standard (functional of Brownian bridge)

Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178.

adf-test phillips-perron-test cointegration-test arima


Kriging

Gaussian process regression (geostatistics)

Georges Matheron (formalised geostatistics)

Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms.

Best linear unbiased predictor (BLUP) from the variogram

Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266.

Cressie, N. (1993). Statistics for Spatial Data (Revised ed.). Wiley.

spatial-panel-model mgwr-model geographically-weighted-regression getis-ord-gi ols-regression


Kruskal-Wallis test

Kruskal-Wallis one-way analysis of variance

The Kruskal-Wallis H test is a nonparametric hypothesis test that compares three or more independent groups to decide whether their distributions (typically their medians) differ. Introduced by William Kruskal and W. Allen Wallis in 1952, it works on ranks rather than raw values and is the distribution-free counterpart to one-way ANOVA.

Kruskal, W. H. & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.

one-way-anova mann-whitney-u friedman-test dunn-test permutation-test


Kullback-Leibler Divergence

KL divergence
relative entropy
information divergence

Kullback-Leibler divergence, also called relative entropy or information divergence, measures the asymmetric difference between two probability distributions. Introduced by Solomon Kullback and Richard Leibler in 1951, this information-theoretic measure quantifies how one probability distribution diverges from a reference distribution, ranging from 0 (identical distributions) to infinity. It is foundational in information theory, machine learning, and decision-making with probabilistic frameworks.

Asymmetric probability distribution dissimilarity

Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.

Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. Wiley-Interscience.


L2T-CODAS (Linguistic extension of L2T-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by He, Y., Wei, G., Chen, X., Zhao, J. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))

He, Y., Wei, G., Chen, X., Zhao, J. (2019). CODAS method for Pythagorean 2-tuple linguistic multiple attribute group decision making. Journal of Intelligent & Fuzzy Systems

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


L2T-COPRAS (Linguistic extension of L2T-COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Gai, T., Cao, M., Cao, Q., Wu, J., Yu, G., Zhou, M. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Gai, T., Cao, M., Cao, Q., Wu, J., Yu, G., Zhou, M. (2021). An integrated method for hybrid distribution with estimation of demand matching degree (interval 2-tuple linguistic COPRAS).


L2T-EDAS (2-Tuple Linguistic Neutrosophic EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, P., Wang, J., Wei, G. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451. [Canonical EDAS source; cited in place of the retracted Wang et al. (2019) 2-tuple linguistic neutrosophic EDAS paper, doi:10.3233/JIFS-179223.]


L2T-MULTIMOORA (2-Tuple Linguistic MULTIMOORA (Balezentis & Balezentis 2011)) is a ranking multi-criteria decision-making (MCDM) method introduced by Baležentis, Alvydas, Baležentis, Tomas in 2006 crisp; 2011 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Linguistic ratio/reference/multiplicative ensemble — 2-Tuple Linguistic Variable (2TL: (s_i, α))

Baležentis, Alvydas, Baležentis, Tomas (2011). An innovative multi-criteria supplier selection based on two-tuple MULTIMOORA and hybrid data. Economic Computation and Economic Cybernetics Studies and Research


L2T-SAW (Linguistic extension of L2T-SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E. (2018). Prioritization of the launch of ICT products and services through linguistic multi-criteria decision-making (2-tuple SAW). Technological and Economic Development of Economy


L2T-TODIM (Linguistic extension of L2T-TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Qi, X., Liang, C., Zhang, J. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Qi, X., Liang, C., Zhang, J. (2021). A collaborative emergency decision making approach based on BWM and TODIM under interval 2-tuple linguistic environment. International Journal of Machine Learning and Cybernetics


L2T-TOPSIS (Linguistic extension of L2T-TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wei, G. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Wei, G. (2010). Models for Multiple Attribute Group Decision Making with 2-Tuple Linguistic Assessment Information. International Journal of Computational Intelligence Systems


L2T-VIKOR (Linguistic extension of L2T-VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Ju, Y., Wang, A. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Ju, Y., Wang, A. (2013). Extension of VIKOR method for multi-criteria group decision making problem with linguistic information. Applied Mathematical Modelling


Label Propagation

Label Propagation (Graph-Based Semi-Supervised Learning)

LP
label spreading
graph-based semi-supervised learning
harmonic label propagation

Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.

Classification on partially labeled graphs

Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.

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

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.

label-spreading graph-neural-network semi-supervised-svm k-nearest-neighbors spectral-clustering random-forest


Laboratory Experiment

lab experiment
controlled experiment
true experiment
lab study

17th century (natural science); ~1879 onward (behavioral/social science)

Francis Bacon, Robert Boyle (early scientific method); formalized in social science by Wilhelm Wundt (1879 psychology lab) and Ronald A. Fisher (20th-century design principles)

A laboratory experiment is a research design in which the investigator systematically manipulates one or more independent variables under tightly controlled conditions, randomly assigns participants to conditions, and measures the effect on dependent variables. By maximizing internal control, the laboratory experiment is the gold standard for establishing cause-and-effect relationships. It is the backbone of experimental psychology, cognitive science, pharmacology, and many social sciences.

Numeric/interval/ratio measurements of dependent variables under controlled conditions

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

randomized-controlled-trial field-experiment factorial-experiment quasi-experiment pretest-posttest-experimental-design control-group-experimental-design


Lactate Threshold (OBLA)

Onset of Blood Lactate Accumulation and Lactate Threshold Assessment

OBLA
anaerobic threshold
lactate turnpoint
maximal lactate steady state

Lactate threshold, also termed the onset of blood lactate accumulation (OBLA), is the exercise intensity at which blood lactate concentration increases rapidly and non-linearly. Initially defined by Klaus Wasserman in 1973, the concept describes the physiological transition from aerobic to anaerobic metabolism. As exercise intensity increases, lactate production and clearance remain balanced until a critical threshold is exceeded, after which lactate rapidly accumulates in the blood, signaling a shift toward anaerobic energy pathways. This parameter is crucial in endurance sports and clinical exercise assessment.

Wasserman, K., Whipp, B. J., Koyal, S. N., & Beaver, W. L. (1973). Anaerobic threshold and respiratory gas exchange during exercise. Journal of Applied Physiology, 35(2), 236-243.

Sjödin, B., & Svedenhag, J. (1985). Applied physiology of marathon running. Sports Medicine, 2(2), 83-99.

Mader, A., Heck, H., & Hollmann, W. (1976). Evaluation of lactic acid threshold from a constant load work test above and below threshold. European Journal of Applied Physiology, 35(3), 169-178.

vo2-max critical-power respiratory-exchange-ratio epoc heart-rate-recovery


Landmark Analysis

Landmark Analysis for Conditional Survival and Dynamic Prediction

landmark method
dynamic prediction
conditional survival estimation
Landmark Analizi (Dinamik Tahmin)

Landmark analysis, introduced by Anderson, Cain, and Gelber in 1983, estimates conditional survival probabilities for subjects who are still at risk at a pre-specified point in time — the landmark — rather than at study entry. It was developed explicitly to avoid immortal time bias that arises when subjects are grouped by an event (such as a treatment change or biomarker result) that can only occur if they remain event-free long enough to experience it.

Right-censoring, time-varying treatment status, immortal time bias

Anderson, J. R., Cain, K. C. & Gelber, R. D. (1983). Analysis of Survival by Tumor Response. Journal of Clinical Oncology, 1(11), 710–719.

van Houwelingen, H. C. (2007). Dynamic Prediction by Landmarking in Event History Analysis. Scandinavian Journal of Statistics, 34(1), 70–85.

kaplan-meier cox-ph joint-model-survival fine-gray-model nelson-aalen


Landscape Metrics

landscape pattern indices
FRAGSTATS metrics
fragmentation indices
peyzaj metrikleri

R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)

Landscape metrics are quantitative indices that describe the composition and spatial configuration of a categorical map — typically land cover — at the patch, class, and whole-landscape levels. Developed in landscape ecology (O'Neill and colleagues, 1988) and made widely usable by the FRAGSTATS software, they turn maps into numbers like patch density, edge density, fragmentation, diversity, and connectivity for ecological, planning, and change analysis.

Quantitative landscape pattern description

O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.

McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-GTR-351.

ca-markov object-based-image-analysis moran-s-i community-detection


Language Identification

language detection
LID
Dil Tanımlama (Language Identification)

Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets.

Character n-gram models / supervised classifiers (e.g. langid.py, fastText)

Output: Predicted language label per document

Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.

Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. (2017). Bag of Tricks for Efficient Text Classification. Proceedings of the EACL 2017.

text-classification spelling-grammar-check ngram-language-model sentiment-analysis


Laplace Approximation

Laplace's method
saddle-point approximation (Bayesian)
second-order Gaussian approximation
LA

Pierre-Simon Laplace (1774); Bayesian formalisation: Tierney & Kadane (1986)

The Laplace approximation is a classical analytic technique that replaces an intractable posterior distribution with a multivariate Gaussian centred at the posterior mode, using the curvature of the log-posterior at that mode to set the covariance. Formalised for Bayesian statistics by Tierney and Kadane (1986) in their landmark Journal of the American Statistical Association paper, it provides a fast, deterministic alternative to Markov chain Monte Carlo and forms the mathematical core of Integrated Nested Laplace Approximations (INLA).

approximate inference / posterior summarisation

Gaussian approximation to posterior / marginal likelihood estimate

O(p³) per evaluation (Hessian inversion)

Tierney, L. & Kadane, J. B. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association, 81(393), 82–86.

MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.

Rue, H., Martino, S. & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71(2), 319–392.

mcmc variational-bayes bayesian-regression hierarchical-bayes expectation-propagation


Large Eddy Simulation

Large Eddy Simulation (LES) is a turbulence modeling technique that explicitly resolves large-scale turbulent eddies while modeling small-scale subgrid-scale (SGS) motions. Introduced by Joseph Smagorinsky in 1963, LES represents a middle ground between Reynolds-Averaged Navier-Stokes (RANS) and Direct Numerical Simulation (DNS). By capturing the energy-containing scales of turbulence, LES provides superior accuracy for transient flows and complex geometries at computational costs significantly lower than DNS.

Smagorinsky, J. (1963). General circulation experiments with the primitive equations: I. The basic experiment. Monthly Weather Review, 91(3), 99-164.

Leonard, A. (1974). Energy cascade in large-eddy simulations of turbulent fluid flows. Advances in Geophysics, 18, 237-248.

Meneveau, C., & Katz, J. (2000). Scale-invariance and turbulence models for large-eddy simulation. Annual Review of Fluid Mechanics, 32, 1-32.

reynolds-averaged-navier-stokes direct-numerical-simulation detached-eddy-simulation lattice-boltzmann-method volume-of-fluid


Lasso Regression

Least Absolute Shrinkage and Selection Operator (LASSO)

LASSO Regresyonu
lasso
L1-regularized regression
L1 regularization

Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.

Regularized linear regression (L1 penalty)

Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.

ridge-regression elastic-net linear-regression logistic-regression pca


Latent Class Analysis

LCA
latent class model
latent categorical analysis
finite mixture of multinomials

Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.

Latent variable / person-centered classification

Categorical (binary or polytomous) observed indicators

Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.

Lazarsfeld, P. F. & Henry, N. W. (1968). Latent Structure Analysis. Houghton Mifflin.

latent-profile-analysis mixture-modeling cluster-analysis exploratory-factor-analysis confirmatory-factor-analysis discriminant-analysis


Latent Diffusion Models

High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695).

masked-autoencoders graphrag detr segment-anything-model


Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA — Blei, Ng & Jordan 2003)

LDA
topic model
Blei-Ng-Jordan model
probabilistic topic modeling
generative topic model

Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.

Generative probabilistic topic model (three-level hierarchical Bayesian)

Unsupervised topic discovery in text corpora

Variational EM or collapsed Gibbs sampling

α (document-topic concentration), β (topic-word concentration), K (number of topics)

Topic-word distributions (Φ) and document-topic distributions (θ)

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9). Springer.

non-negative-matrix-factorization probabilistic-latent-semantic-analysis word2vec hidden-markov-model naive-bayes-classifier k-means-clustering


LGC Model

latent growth model
LGC
growth curve model
Gizil Büyüme Eğrisi Modeli

The latent growth curve model is a structural equation modelling approach introduced by Meredith and Tisak (1990) for analysing change over time. It treats each individual's starting point (intercept) and rate of change (slope) as latent variables, simultaneously estimating the average trajectory across the sample and the extent to which individuals differ in their own trajectories.

Latent variable / longitudinal growth model

Continuous repeated measures (longitudinal / panel)

Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122.

confirmatory-factor-analysis sem mixed-effects-model repeated-measures-anova exploratory-factor-analysis