Latent Profile Analysis

Continuous Latent Class Analysis
Gaussian Profile Mixture Model
Person-Centered Cluster Analysis
Gizil Profil Analizi

Latent Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns of scores across multiple continuous indicators. Rooted in Lazarsfeld and Henry's latent structure tradition and formally synthesized for applied behavioral research by Collins and Lanza (2010), LPA assumes that observed heterogeneity in continuous data arises from a discrete number of latent classes, each characterized by a unique multivariate mean profile.

Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley.


Latent Transition Analysis

Latent Transition Analysis (LTA) is a method for studying transitions between latent classes over time, developed by Collins and Lanza (2010). LTA combines latent class analysis (grouping individuals into classes) with Markovian transition models to understand how people move between qualitatively distinct states across time periods.

Markovian transition between latent states

Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. Wiley.

Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LTA: A SAS macro for latent transition analysis. Structural Equation Modeling, 14(4), 671-694.

Vermunt, J. K., & Magidson, J. (2016). Latent class and latent transition analysis. In J. P. Baltes, G. G. Brim, D. Featherman, & S. Shye (Eds.), Lifespan Development and Behavior (pp. 91-113). Academic Press.


Latin Hypercube Sampling

Latin Hypercube Sampling and Sensitivity Analysis

Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi

Latin Hypercube Sampling (LHS) is a stratified space-filling design for computer experiments, introduced by McKay, Beckman, and Conover in 1979. It divides each input variable's range into equally probable strata and draws exactly one sample per stratum, ensuring that the full input space is covered with far fewer model evaluations than standard Monte Carlo simulation requires. It is routinely paired with global sensitivity analysis — particularly Sobol indices — to quantify how much each input drives output variability.

Stratified space-filling sampling design

Sample matrix over input space; Sobol sensitivity indices

Global sensitivity analysis (Sobol indices, Iman-Conover correlation control)

McKay, M.D., Beckman, R.J. & Conover, W.J. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245.

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.

monte-carlo-simulation quasi-monte-carlo variance-reduction-mc sobol-sensitivity-analysis bootstrap-simulation design-of-experiments


Latin Square Design

Latin Square and Greco-Latin Square Design

Latin Square
Greco-Latin Square
Latin Kare ve Greco-Latin Kare Deseni

The Latin square design is a blocked experimental design that simultaneously controls two independent nuisance factors — the row block and the column block — so that each treatment appears exactly once in every row and every column of an n×n arrangement. Formalised by Ronald A. Fisher in his 1935 monograph The Design of Experiments, the design dramatically reduces experimental error by absorbing variation from two extraneous sources before the treatment effects are estimated.

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

Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.

randomized-complete-block-design one-way-anova two-way-anova split-plot-design factorial-design crossover-design


Lattice-Based Cryptography

lattice cryptography
post-quantum lattice cryptography

Lattice-based cryptography is a class of cryptosystems whose security is derived from the computational hardness of lattice problems, particularly the shortest vector problem (SVP) and learning with errors (LWE). First proposed by Miklós Ajtai in 1996, lattice-based approaches have gained prominence as the leading candidates for post-quantum cryptography. Unlike RSA and ECC, which are vulnerable to quantum computers, lattice problems are believed to remain hard even against quantum algorithms.

public-key cryptosystem based on lattice hardness

Ajtai, M. (1996). Generating hard instances of the short basis problem. In Proceedings of the 28th Annual ACM Symposium on Theory of Computing, pp. 99-108.

Regev, O. (2005). On lattices, learning with errors, hard instances, and public key cryptography. In Proceedings of STOC 2005, pp. 84-93.


Lattice Boltzmann Method

The Lattice Boltzmann Method (LBM) is a kinetic theory-based computational approach to fluid dynamics that discretizes the Boltzmann equation on a lattice grid. Developed by McNamara and Zanetti in 1988, LBM computes fluid behavior by tracking the distribution of particle velocities at discrete lattice nodes rather than solving the Navier-Stokes equations directly. This method naturally incorporates complex physics (turbulence, multiphase flows, porous media) and is highly parallelizable, making it increasingly popular for modern computational platforms.

McNamara, G. R., & Zanetti, G. (1988). Use of the Boltzmann equation to simulate lattice-gas automata. Physical Review Letters, 61(20), 2332-2335.

Qian, Y. H., d'Humières, D., & Lallemand, P. (1992). Lattice BGK models for the Navier-Stokes equation. Europhysics Letters, 17(6), 479-484.

Chen, S., & Doolen, G. D. (1998). Lattice Boltzmann method for fluid simulations. Annual Review of Fluid Mechanics, 30, 329-364.

direct-numerical-simulation large-eddy-simulation smoothed-particle-hydrodynamics volume-of-fluid reynolds-averaged-navier-stokes


Lattice QCD

Lattice Quantum Chromodynamics (LQCD) is a computational method for studying quantum chromodynamics (QCD)—the theory of strong nuclear forces—by discretizing spacetime onto a lattice and simulating quark and gluon dynamics. Introduced by Kenneth Wilson in 1974, LQCD is the only known approach for non-perturbative calculations of QCD properties from first principles.

Wilson, K. G. (1974). Confinement of quarks. Physical Review D, 10, 2445–2459.

Aoki, S., et al. (2020). Flag review 2019. European Physical Journal C, 80, 113.

Durr, B., et al. (2008). Ab initio determination of light hadron masses. Science, 322, 1224–1227.


Lawton-Brody Instrumental ADL Scale

IADL Scale
Lawton IADL
Instrumental Activities of Daily Living

The Lawton-Brody Instrumental Activities of Daily Living (IADL) Scale, developed by M. Powell Lawton and Elaine M. Brody in 1969, measures the capacity to perform complex, higher-order self-care and household tasks necessary for independent community living. The scale assesses eight domains (for women) or five domains (for men): telephone use, shopping, food preparation, housekeeping, laundry, transportation, medication management, and financial management. It complements basic ADL assessment (measured by the Katz Index) and is essential for comprehensive geriatric evaluation.

Clinician-rated or interview-based functional assessment

Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist, 9(3), 179-186.

Lawton, M. P. (1988). Scales to measure competence in basic and instrumental ADL. Psychopharmacol Bull, 24(4), 615-623.


LBWA

LBWA (Level Based Weight Assessment) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Žižović, M., Pamučar, D. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Level-partitioned criterion grouping with influence function weighting

Žižović, M., Pamučar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering

ahpsort aploco aras aroman artasi cobra cocoso codas


LCA

Gizil Sınıf Analizi (LCA)
latent class model
latent structure analysis

Latent class analysis is a probabilistic model-based clustering technique that identifies unobserved subgroups — latent classes — within a population on the basis of patterns of categorical, binary, or ordinal indicator responses. Originating in sociological measurement theory with Lazarsfeld's latent structure work around 1950 and formalised computationally by Goodman in the 1970s, it is widely used in the social, health, and behavioural sciences to reveal hidden population heterogeneity.

Latent variable / probabilistic clustering

Discrete latent class memberships with posterior probabilities

Categorical / binary / ordinal indicators

Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press.

Nylund, K. L., Asparouhov, T. & Muthen, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. Structural Equation Modeling, 14(4), 535–569.

exploratory-factor-analysis cluster-analysis mixture-models lpa sem


LD Block Analysis

Linkage Disequilibrium Block Analysis and Haplotype Mapping

Haplotype block analysis
LD mapping
Block structure analysis

Linkage disequilibrium (LD) block analysis is a genomic method that partitions the human genome into distinct haplotype blocks—regions of limited recombination where variants are in strong statistical association. First systematically described by Gabriel and colleagues in 2002, this approach reveals the underlying structure of genetic variation and enables efficient genomic studies by reducing the number of variants needed to capture common diversity. LD block analysis forms the foundation of genome-wide association study (GWAS) design and modern population genetics.

Gabriel, S. B., Schaffner, S. F., Nguyen, H., Moore, J. M., Roy, J., Blumenstiel, B., & Lander, E. S. (2002). The structure of haplotype blocks in the human genome. Science, 296(5576), 2225–2229.

Daly, M. J., Rioux, J. D., Schaffner, S. F., Hudson, T. J., & Lander, E. S. (2001). High-resolution haplotype structure in the human genome. Nature Genetics, 29(2), 229–232.

Wang, N., Akey, J. M., Zhang, K., Chakraborty, R., & Jin, L. (2005). Distribution of recombination crossovers and the origin of block-like patterns of linkage disequilibrium. Genetics, 155(4), 1599–1606.

ibd-mapping qtl-mapping f-statistics polygenic-risk-score admixture-analysis


Linear Discriminant Analysis (Classification)

Linear Discriminant Analysis (LDA — Classification)

Doğrusal Diskriminant Analizi (LDA — Sınıflandırma)

Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.

Parametric linear classifier / dimensionality reduction

min(K−1, p) where K = classes, p = features

Fisher's ratio — between-class to within-class scatter

Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.

manova logistic-regression qda-classification pca factor-analysis svm-classification naive-bayes knn


LDA Topic Model

LDA
Latent Dirichlet Allocation
LDA Topic Modeling
Dirichlet Topic Model

Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.

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

nmf-topic-model topic-modeling sentence-embeddings bert-based-classification lsa word2vec


LDPC Codes

LDPC codes, invented by Robert Gallager in 1962 and rediscovered in the 1990s by MacKay, are linear error-correcting codes defined by sparse parity-check matrices. They achieve performance within 0.4 dB of the Shannon limit with iterative belief-propagation decoding and have become the standard for modern wireless (WiFi-6, 5G NR, Digital Video Broadcasting). Unlike turbo codes, LDPC codes have a more elegant graph-theoretic structure and more mature theoretical analysis.

Gallager, R. G. (1962). Low-density parity-check codes. IRE Transactions on Information Theory, 8(1), 21-28.

Richardson, T. J., & Urbanke, R. L. (2001). The capacity of low-density parity-check codes under message-passing decoding. IEEE Transactions on Information Theory, 47(2), 599-618.

turbo-code polar-codes shannon-capacity ofdm mimo


Leader-Member Exchange Scale

The Leader-Member Exchange Scale (LMX-7) measures the quality of the working relationship between a supervisor and employee. Developed by Graen and Uhl-Bien in 1995, it is a brief, widely adopted instrument grounded in Leader-Member Exchange theory. The scale captures mutual trust, respect, and obligation—the psychological foundation of effective working relationships. Higher LMX quality predicts engagement, performance, and retention.

Graen, G. B., & Uhl-Bien, M. (1995). Relationship-based approach to leadership: Development of leader-member exchange (LMX) theory of leadership over 25 years. Leadership Quarterly, 6(2), 219–247.

Liden, R. C., Maslyn, J. M., & Hallenbeck, J. R. (1998). Multidimensionality of leader-member exchange: The construction and validation of a multidimensional measure. Journal of Management, 24(2), 211–235.

Schriesheim, C. A., Castro, S. L., & Cogliser, C. C. (1999). Leader-member exchange (LMX) research: A comprehensive review of theory, measurement, and data-analytic practices. Leadership Quarterly, 10(1), 63–113.


Leaf Area Index

Leaf Area Index (LAI) Measurement and Applications

Leaf Area Index (LAI) is a dimensionless quantity that measures the total one-sided area of leaves per unit ground area covered by a canopy. It quantifies canopy density and structure: LAI = 0 for bare soil, LAI = 1 for a thin crop, LAI = 3-6 for dense cereal or grass canopies, and LAI > 8 for dense forest. LAI is a key variable in crop growth models, evapotranspiration estimation, and remote sensing because it directly controls light interception, photosynthesis, and water loss from vegetation.

Watson, D. J. (1947). Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany, 11(43), 375-407.

Chen, J. M., & Black, T. A. (1992). Defining leaf area index for non-flat leaves. Plant, Cell & Environment, 15(4), 421-429.

Weiss, M., Baret, F., Smith, G. J., Jonckheere, I., & Coppin, P. (2004). Review of methods for in situ leaf area index (LAI) determination: Part II. LiDAR and spectral approaches. Agricultural and Forest Meteorology, 121(1-2), 37-53.


Lean Healthcare

Lean Management Principles Applied to Healthcare Operations

Lean Healthcare Management
Healthcare Lean

Process improvement, Operational efficiency

Lean is a management philosophy that emerged from the Toyota Production System, focused on maximizing patient value while minimizing waste. Applied to healthcare, Lean uses systematic methods to identify and eliminate non-value-added activities, reduce wait times, and improve the quality of patient care.

Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production. Productivity Press.

Womack, J. P., Jones, D. T., & Roos, D. (1990). The Machine that Changed the World. Rawson Associates.

Kim, C. S., Spahlinger, D. A., Kin, J. M., & Billi, J. E. (2009). Lean health care: What can hospitals learn from a world-class automaker? Journal of Hospital Medicine, 4(3), 191–199.

six-sigma-healthcare patient-flow-simulation dea-hospital-efficiency balanced-scorecard-healthcare hospital-readmission-model


Learning Analytics

Educational Data Mining
Academic Analytics
Learning Data Analytics
Öğrenme Analitiği

Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, with the purpose of understanding and optimizing learning and the environments in which it occurs. Formally introduced by George Siemens and Phil Long in 2011, the approach draws on data generated in digital learning environments to provide educators, institutions, and learners with evidence-based feedback for improving educational outcomes.

data-driven educational process pipeline

digital learning environments (LMS, MOOCs, e-portfolios)

Output: actionable insights for instructors, learners, and institutions

Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.


Learning Curve

Power Law of Practice
Experience Curve
Wright's Law
Öğrenme Eğrisi

The learning curve models how performance improves predictably as cumulative experience accumulates. Formalized by Theodore Wright in 1936 using aircraft manufacturing data, it expresses the relationship between the number of practice trials (or production units) and the time or cost per unit as a power-law function. It is widely applied in educational psychology, industrial engineering, health professions training, and human factors research whenever repeated task execution is the mechanism of skill acquisition.

Cumulative production or practice trial counts with paired performance measurements

Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128.


Least-Cost Path

Least-Cost Path / Cost-Distance Analysis

cost-distance analysis
accumulated cost surface
least-cost corridor
en düşük maliyetli yol

Edsger Dijkstra (shortest path); GIS cost-surface adaptation

Least-cost path analysis finds the route between two locations that minimizes accumulated travel cost across a landscape, rather than minimizing straight-line distance. By encoding terrain, slope, land cover, and other frictions into a cost surface and accumulating cost outward from a source, it identifies optimal corridors for roads, pipelines, trails, power lines, and wildlife movement — a core raster-GIS technique built on Dijkstra's shortest-path logic.

Input: Cost (friction) surface

Output: Least-cost route / corridor

Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.

Douglas, D. H. (1994). Least-cost path in GIS using an accumulated cost surface and slopelines. Cartographica, 31(3), 37–51.

location-allocation kriging gis-mcda ca-markov


Least Median of Squares

LMS
least median of squares regression
en küçük medyan kareler (LMS)

Least Median of Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of minimising the sum of squared residuals like ordinary least squares, it minimises the median of the squared residuals, which lets the fit resist contamination by up to roughly 50% outliers.

Minimises the median of squared residuals

Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.

Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Functions. Wiley.

ols-regression quantile-regression theil-sen-estimator ransac-regression least-trimmed-squares


Least Trimmed Squares

LTS
least trimmed squares regression
trimmed least squares
robust regression
En Küçük Kırpılmış Kareler (LTS)

Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.

Minimisation of the sum of the smallest h squared residuals

Rousseeuw, P. J., & Van Driessen, K. (2006). Computing LTS Regression for Large Data Sets. Data Mining and Knowledge Discovery, 12, 29-45.

ols-regression least-median-squares ransac-regression theil-sen-estimator quantile-regression


Lee-Carter Model

Singular Value Decomposition Mortality Model

The Lee-Carter model is a stochastic framework for modeling and forecasting age-specific mortality rates, introduced by Ronald Lee and Lawrence Carter in their landmark 1992 paper. It decomposes the logarithm of age-specific death rates into an age pattern of mortality, a time-varying index of mortality level, and an age-specific sensitivity of that index, then forecasts the time index using ARIMA time-series methods to generate probabilistic mortality projections.

Age-specific death rates over multiple calendar years

Singular value decomposition (SVD) plus ARIMA time-series forecasting

Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87(419), 659–671.


Lee-Strazicich Test

Lee-Strazicich LM Unit-Root Test with Two Breaks

LS Unit Root Test
Minimum LM Unit Root Test
Lee-Strazicich Two-Break Test
Lee-Strazicich LM Testi

The Lee-Strazicich (2003) test is a Lagrange Multiplier-based unit-root test that allows for two endogenous structural breaks under both the null and alternative hypotheses. Proposed by Junsoo Lee and Mark C. Strazicich, it corrects a fundamental flaw in earlier break-based tests such as Zivot-Andrews, where structural breaks were permitted only under the alternative. By incorporating breaks under the null, the LS test avoids spurious rejections and provides size-correct inference in the presence of level or trend shifts.

Lagrange Multiplier unit-root test with two endogenous structural breaks

Series has a unit root with two structural breaks

Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089.


LANSS

LANSS Pain Scale
Leeds Neuropathic Symptoms

neuropathic pain screening and assessment

The LANSS is a brief seven-item hybrid screening and diagnostic tool designed to differentiate neuropathic pain from non-neuropathic (nociceptive) pain. Developed by Mark Bennett at the University of Leeds in 2001, it combines five patient-reported symptom items with two clinician-performed neurological examination findings. With a sensitivity of 82% and specificity of 80%, LANSS is among the most accurate tools for identifying whether a patient's pain has a neuropathic component, making it invaluable in clinical practice and research settings where pain etiology classification is essential.

Hybrid questionnaire and clinician examination

Bennett, M. I. (2001). The LANSS Pain Scale: The Leeds Assessment of Neuropathic Symptoms and Signs. Pain, 92(1-2), 147-157.

neuropathic-pain-symptom-inventory migraine-disability-assessment stroke-specific-qol alsfrs-r


Legal Content Analysis

LCA
legal text analysis
jurimetric content analysis
statutory content analysis

1940s–1970s (applied systematically to legal texts)

Interdisciplinary; foundational content analysis by Harold Lasswell (1940s); applied to legal texts by empirical legal scholars from the 1970s onward

Legal content analysis applies the systematic procedures of content analysis to legal texts — statutes, regulations, judicial opinions, treaties, and legal commentaries — in order to identify patterns, themes, and trends across a corpus of legal material. It bridges qualitative legal scholarship and quantitative social-science methods, enabling researchers to draw reproducible, evidence-based conclusions about how law is written, applied, or has changed over time.

Systematic qualitative-quantitative text analysis

Legal texts: statutes, regulations, court opinions, treaties, contracts, legal commentaries

Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage Publications.

Nourse, V., & Shaffer, G. (2014). Varieties of New Legal Realism: Can a New World Order Prompt a New Legal Theory? Cornell Law Review, 95(1), 61–137.

doctrinal-legal-research comparative-legal-analysis case-law-analysis content-analysis discourse-analysis thematic-analysis


Legal Judgment Prediction

Legal Judgment Prediction using Machine Learning

court outcome prediction
judicial decision prediction
legal AI forecasting

Machine learning and artificial intelligence

Legal judgment prediction is a machine learning approach that forecasts court decisions and judicial outcomes based on case features, legal precedent, and judicial characteristics. Pioneered by Daniel Katz and colleagues in 2017 with their celebrated U.S. Supreme Court prediction model, this method applies supervised learning to large datasets of digitized court decisions to identify patterns in how judges decide cases. Legal judgment prediction has since expanded to appellate courts, trial courts, and international tribunals, enabling legal professionals to anticipate case outcomes and make strategic litigation decisions.

Computational law and judicial decision prediction method

Katz, D. M., Bommarito, M. J., & Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PLOS One, 12(4), e0174698.

Matz, D., & Spicer, J. (2019). Predicting judicial decisions of the European Court of Human Rights. Artificial Intelligence and Law, 27(2), 123-145.

Lage-Freitas, A., de Oliveira Santini, F., Praxedes Filho, P. H., & de Almeida Oliveira, A. (2022). Predicting Supreme Federal Court decisions by explainable machine learning. Frontiers in Artificial Intelligence, 4, 586561.


Lerchs-Grossmann Algorithm

Lerchs-Grossmann Algorithm for Open Pit Mine Design

The Lerchs-Grossmann Algorithm is a graph-theoretic method for determining the ultimate pit limit in open-pit mining operations. Introduced by Helmut Lerchs and Israel Grossmann in 1965, it maximizes the net present value of extracted ore while respecting slope stability constraints. This algorithm forms the theoretical foundation for most modern pit optimization software.

Graph-theoretic algorithm for pit limit optimization

Lerchs, H., & Grossmann, I. F. (1965). Optimum design of open-pit mines. Canadian Mining and Metallurgical Bulletin, 58(633), 47-54.

Johnson, T. B. (2014). Optimum pit limits - definition and computational procedures. Journal of the Australasian Institute of Mining and Metallurgy, 249, 21-28.

pseudoflow cut-off-grade stope-layout mine-ventilation


Leslie Matrix

Leslie model
age-structured population model
matrix population model
population dynamics

The Leslie matrix is a deterministic model of age-structured population dynamics, introduced by Patrick Leslie (1945). It projects population size and structure forward in time using age-specific fertility and survival rates. A Leslie matrix encodes these vital rates in a square matrix; multiplying the matrix by a population vector yields the population's composition at the next time step. This approach enables calculation of the population's asymptotic growth rate (λ), identification of stable age structure, and sensitivity analysis—understanding which vital rates most strongly influence population growth.

Leslie, P. H. (1945). On the use of matrices in certain population mathematics. Biometrika, 33(3), 183-212.

Caswell, H. (2001). Matrix Population Models: Construction, Analysis, and Interpretation. Sinauer Associates, Sunderland, Massachusetts.

Easterling, M. R., Ellner, S. P., & Dixon, P. M. (2000). Size-specific sensitivity: applying a new structured population model. Ecology, 81(3), 694-708.

population-viability-analysis integral-projection-model distance-sampling siar-mixing-model


Lesson Study

Jugyou Kenkyuu
LS
collaborative lesson research
teaching study

Late 19th century Japan; international dissemination from 1999

Japanese elementary school teachers (formalized); introduced to Western research by James Stigler & James Hiebert

Lesson study is a structured, cyclical form of professional development and educational research in which a team of teachers collaboratively plans a single 'research lesson,' observes it live in a classroom, analyzes student learning in detail, revises the lesson, and shares findings with the broader teaching community. Originating in Japanese elementary schools and brought to international attention by Stigler and Hiebert's 1999 comparative study, it has become one of the most widely adopted teacher-led inquiry methods worldwide.

Collaborative practitioner inquiry / professional development research

Classroom observation notes, video recordings, student work samples, teacher reflection journals

Stigler, J. W., & Hiebert, J. (1999). The Teaching Gap: Best Ideas from the World's Teachers for Improving Education in the Classroom. Free Press.

Lewis, C. C. (2002). Lesson Study: A Handbook of Teacher-Led Instructional Change. Research for Better Schools.

action-research educational-action-research classroom-observation design-based-research curriculum-analysis teacher-professional-development


Letter to the Editor

Letter to the Editor (Rapid Communication and Response to Published Work)

correspondence
editor response
rapid letter
technical comment

A letter to the editor is a brief, rapid communication (typically <500 words) published in academic journals, usually in response to a recently published article. Letters enable scholars to raise questions, offer corrections, present supporting or contrary evidence, or highlight implications of published work. Unlike full research articles, letters are faster to publish (weeks to months), making them valuable for timely scientific discourse. Letters are indexed in major databases (PubMed, Scopus, Web of Science) and count as publications, though carrying lower weight than original research articles. The letter format dates to the earliest academic journals and remains a vital vehicle for scholarly dialogue.

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

American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.

Committee on Publication Ethics (2023). Guidelines on Good Publication Practice. https://publicationethics.org

original-research-article editorial-commentary peer-review-process academic-debate


LSI-R

LSI-R
LSI-R-SV
Andrews-Bonta Risk Assessment

The Level of Service Inventory-Revised (LSI-R) is a 54-item assessment instrument developed by Andrews and Bonta (1995) to measure offender risk level and criminogenic needs (dynamic risk factors related to criminal behavior) in criminal justice populations. It is grounded in the Risk-Need-Responsivity (RNR) model of offender rehabilitation and is widely used in correctional facilities, probation/parole services, and forensic settings to inform release decisions, supervision intensity, treatment prioritization, and rehabilitation planning.

Andrews, D. A., & Bonta, J. (1995). The Level of Service Inventory-Revised. Department of Psychology, Carleton University.

Bonta, J., Law, M., & Hanson, K. (2007). The prediction of criminal and violent recidivism among mentally disordered offenders: A meta-analysis. Psychological Bulletin, 123(2), 123–142.

hcr-20 violence-risk-appraisal-guide saprof psychopathy-checklist-screening


Level Set Method

The Level Set Method is an implicit interface tracking technique introduced by Osher and Sethian in 1988 for moving boundary problems and multiphase flows. Rather than explicitly tracking the interface, level sets represent it as the zero level set (contour) of a signed distance function φ. This approach elegantly handles topological changes, naturally computes interface curvature and normals, and integrates well with Eulerian solvers. Level sets have become essential for image processing, shape optimization, and interface-dominated fluid dynamics problems.

Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1), 12-49.

Sethian, J. A. (1996). Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press.

Sussman, M., Smereka, P., & Osher, S. (1994). A level set approach for computing solutions to incompressible two-phase flow. Journal of Computational Physics, 114(1), 146-159.

volume-of-fluid eulerian-lagrangian-model large-eddy-simulation direct-numerical-simulation boundary-layer-theory


Levelized Cost of Energy

Levelized Cost of Energy Analysis for Thermal Systems

Levelized Cost of Energy (LCOE) is a standardized metric that spreads the total lifecycle cost of an energy project over its lifetime energy output. It enables fair comparison of electricity generation technologies with different capital structures, operating costs, and lifetimes. LCOE is widely used for technology evaluation, investment decisions, and energy policy analysis.

Lazard. (2023). Levelized Cost of Energy Analysis (v17.0). Lazard Ltd.

Cole, W., Frazier, A. W., & Augustine, C. (2020). Cost Projections for Utility-Scale Battery Storage. National Renewable Energy Laboratory (NREL), Technical Report NREL/TP-5700-75385.


Levene and Brown-Forsythe Test

Levene and Brown-Forsythe Test for Equality of Variances

Levene test
Brown-Forsythe test
homogeneity of variance test
Levene ve Brown-Forsythe Varyans Testi

Howard Levene; Morton B. Brown and Alan B. Forsythe

The Levene and Brown-Forsythe test checks whether two or more groups share the same variance (homogeneity of variance). Levene (1960) built the test on absolute deviations from each group mean, and Brown and Forsythe (1974) made it robust to non-normal data by centring on the group median instead.

F-test on absolute deviations from group centre (mean for Levene, median for Brown-Forsythe)

Levene, H. (1960). Robust Tests for Equality of Variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Stanford University Press.

Brown, M. B. & Forsythe, A. B. (1974). Robust Tests for the Equality of Variances. Journal of the American Statistical Association, 69(346), 364-367.

bartlett-test one-way-anova welch-anova permutation-test bootstrap-inference


Levenshtein Distance

edit distance
Damerau-Levenshtein distance

Levenshtein distance, also called edit distance, measures the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another. Introduced by Vladimir Levenshtein in 1966, this metric is a true metric (satisfying all distance properties) and is fundamental in computational linguistics, spell checking, DNA sequence comparison, and record linkage. It ranges from 0 (identical strings) to the length of the longer string.

Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10, 707-710.

Damerau, F. J. (1964). A technique for computer detection and correction of spelling errors. Communications of the ACM, 7(3), 171-176.


Levin-Lin-Chu Test

Levin-Lin-Chu (LLC) Panel Unit-Root Test

LLC Test
Panel Unit-Root Test (Homogeneous)
Levin-Lin Unit-Root Test
Panel Birim Kök Testi (LLC)

Andrew Levin, Chien-Fu Lin & Chia-Shang Chu

The Levin-Lin-Chu (LLC) test, introduced by Levin, Lin, and Chu (2002), is a first-generation panel unit-root test that pools cross-sectional information to test whether all units in a panel share a common autoregressive unit root. It is widely used in applied economics and finance when researchers work with balanced or near-balanced panels and require a powerful test against a homogeneous stationary alternative.

Panel unit-root test (homogeneous alternative)

All cross-sectional units share a common unit root (rho = 1)

Standard normal (asymptotic), finite-sample corrections applied

Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.


Lexical Decision Task

Lexical Decision
Word Recognition Task
Lexicality Judgment

The Lexical Decision Task is a computerized measure of word recognition and semantic processing. Participants judge whether letter strings are real words or nonwords (pronounceable but meaningless letter combinations). Response times and accuracy reveal how quickly people access word meanings, how semantic relatedness facilitates recognition, and how word properties (frequency, length, concreteness) influence processing. The task is foundational in cognitive psychology and psycholinguistics for studying lexical representation.

Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90(2), 227-234.

Balota, D. A., Yap, M. J., Cortese, M. J., et al. (2007). The English Lexicon Project. Behavior Research Methods, 39(3), 445-459.

Yap, M. J., Sibley, D. E., Balota, D. A., Ratcliff, R., & Rueckl, J. G. (2015). Insights into lexical processing via large-scale crowdsourcing. PLoS ONE, 10(3), e0119616.

semantic-priming word-recognition reaction-time-analysis orthographic-processing


Lexical Diversity

lexical richness
vocabulary richness
Sözcüksel Çeşitlilik Analizi

Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures.

Text quantification / lexical richness measurement

Type-token ratio (TTR), MTLD, Yule's K, vocd-D

Input: Tokenised text

Output: Lexical-diversity scores per text

McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392.

Tweedie, F. J. & Baayen, R. H. (1998). How Variable May a Constant Be? Measures of Lexical Richness in Perspective. Computers and the Humanities, 32(5), 323-352.


Lexical Substitution

Lexical Substitution (Context-Sensitive Word Replacement)

sözcüksel ikame
Sözcüksel İkame (Lexical Substitution)
context-aware synonym replacement
word-level paraphrasing

McCarthy & Navigli (SemEval shared task, 2007/2009)

Lexical substitution is a natural-language-processing task — formalised by McCarthy and Navigli through the SemEval shared task series starting in 2007 — that replaces a target word in a sentence with a semantically equivalent alternative that preserves the meaning of the surrounding context. It draws on synonym resources such as WordNet or on distributional word embeddings and masked language models to generate and rank candidate replacements, and is used for text robustness testing, style adaptation, and training-data augmentation.

Input: Tokenised text corpus (cross-sectional or longitudinal)

Output: Corpus with target words replaced by context-appropriate alternatives

WordNet synsets / distributional word embeddings / masked language models (e.g., BERT fill-mask)

McCarthy, D. & Navigli, R. (2009). The English Lexical Substitution Task. Language Resources and Evaluation, 43(2), 139-159.

Zhou, W. et al. (2019). BERT for Context-Aware Lexical Substitution. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 7557-7564.

sentiment-analysis text-augmentation word-embeddings bert-embeddings paraphrase-detection named-entity-recognition


Lexicographic Best Worst Method

Lexicographic Best Worst Method (Lexicographic BWM)

Based on Rezaei's BWM framework and lexicographic optimization

Lexicographic BWM combines the strengths of the Best Worst Method with lexicographic (sequential) optimization. Instead of weighting all criteria simultaneously, it assigns criteria to priority levels, solves the BWM for the highest-priority criteria first, then solves for lower-priority criteria while keeping the higher-priority weights fixed. This ensures that higher-priority criteria are never sacrificed to improve lower-priority ones.

Sequential best-worst comparisons with priority hierarchy

Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.

Khanmohammadi, E., & Kazemi, M. (2019). A preference aggregation method based on the comparative advantage of each individual. Applied Soft Computing, 75, 298-310.

bwm lexicographic-goal-programming stratified-bwm fuzzy-bwm lexicographic-fuzzy-programming


Lexicographic Goal Programming

Lexicographic Goal Programming (LGP) is a variant of goal programming introduced by Charnes and Cooper in the 1960s. It prioritizes multiple goals in a strict ordinal hierarchy, solving optimization problems sequentially: first achieve the highest-priority goal, then the second-highest while maintaining the first, and so on. This ensures that lower-priority goals are never pursued at the expense of higher-priority ones.

Sequential goal optimization with priority levels

Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. Management Science, 8(1), 38-91.

Ijiri, Y. (1965). Management goals and accounting for control. North-Holland Publishing Co.

Ignizio, J. P. (1976). Goal programming and extensions. Lexington Books.

goal-programming weighted-goal-programming lexicographic-bwm lexicographic-fuzzy-programming


Lexicon-Based Sentiment Analysis

dictionary-based sentiment analysis
rule-based sentiment scoring
Sözlük Tabanlı Duygu Analizi

Lexicon-based sentiment analysis computes sentiment at the word level using prebuilt sentiment dictionaries such as AFINN (Nielsen, 2011), SentiWordNet, VADER (Hutto & Gilbert, 2014), and the NRC Emotion Lexicon. It scores text by looking words up in a dictionary of charged terms, so it requires no labelled training data.

Lexicon-based NLP sentiment-scoring task

AFINN / SentiWordNet / VADER / NRC Emotion Lexicon

Not required (unsupervised, dictionary-driven)

Output: Word-level and document-level sentiment scores

Nielsen, F.Å. (2011). A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs. Proceedings of the ESWC Workshop on 'Making Sense of Microposts'.

Hutto, C.J. & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 8(1), 216-225.


Libor Market Model

LIBOR Market Model (Brace-Gatarek-Musiela)

Alan Brace, Dariusz Gatarek, and Marek Musiela

The LIBOR Market Model (BGM), developed by Brace, Gatarek, and Musiela (1997), is a multi-factor interest rate model that directly models forward LIBOR rates as lognormal processes. Unlike short-rate models, LMM naturally prices caplets at the market level and is the industry standard for valuing caps, floors, and exotic interest rate derivatives.

Brace, A., Gatarek, D., & Musiela, M. (1997). The market model of interest rate dynamics. Mathematical Finance, 7(2), 127-155.

Jamshidian, F. (1997). LIBOR and swap market models and measures. Finance and Stochastics, 1(4), 293-330.

hjm-framework hull-white-model risk-neutral-valuation change-of-numeraire


LiDAR Analysis

Light Detection and Ranging
Airborne Laser Scanning
Terrestrial Laser Scanning
LiDAR Nokta Bulutu Analizi

LiDAR (Light Detection and Ranging) Point-Cloud Analysis is an active remote sensing technique that measures distances by emitting laser pulses and recording the time for returns to reach the sensor. First systematically applied to ecosystem science by Lefsky, Cohen, Parker, and Harding in 2002, LiDAR produces dense three-dimensional point clouds that encode the precise vertical and horizontal structure of vegetation, terrain, and built environments at resolutions unachievable by passive optical sensors.

Output: Canopy/terrain structural metrics

Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52(1), 19–30.


Liebowitz Social Anxiety Scale

The Liebowitz Social Anxiety Scale (LSAS) is a 24-item clinician-administered scale designed to measure the severity of social anxiety and avoidance in individuals with social anxiety disorder. Developed by Michael R. Liebowitz in 1987, the LSAS has become the gold-standard instrument for assessing social phobia in clinical trials and research settings, particularly valued for its dual measurement of fear and avoidance across diverse social situations.

Liebowitz, M. R. (1987). Social phobia. In Modern Problems in Pharmacopsychiatry (Vol. 22, pp. 141-173). Karger.


Life Cycle Assessment

Life Cycle Analysis
Cradle-to-Grave Analysis
Ecobalance
Yaşam Döngüsü Değerlendirmesi

Life Cycle Assessment is a systematic, ISO-standardized methodology for quantifying the environmental impacts of a product, process, or service across its entire life span — from raw material extraction through production, use, and end-of-life disposal. Codified in ISO 14040 and ISO 14044, and comprehensively reviewed by Finnveden et al. (2009), LCA enables decision-makers to compare alternatives, identify environmental hotspots, and support eco-design, with applications spanning products, buildings, energy systems, and public policy.

Environmental impact accounting pipeline

Finnveden, G., et al. (2009). Recent developments in life cycle assessment. Journal of Environmental Management, 91(1), 1–21.


Life Cycle Sustainability Assessment

Life Cycle Sustainability Assessment (LCSA) is a comprehensive framework developed by Matthias Finkbeiner and colleagues to evaluate environmental, social, and economic impacts of products and services throughout their entire life cycle. Introduced around 2008, it extends traditional life cycle assessment to address sustainability holistically.

Finkbeiner, M., Schau, E. M., Lehmann, A., & Traverso, M. (2010). Towards Life Cycle Sustainability Assessment. Sustainability, 2(10), 3309-3322.

Klöpffer, W. (2008). Towards Life Cycle Sustainability Assessment of Products. Journal of Cleaner Production, 16(17), 1844-1853.

UNEP (2012). Towards a Life Cycle Sustainability Assessment: Making informed choices on products. UNEP Life Cycle Initiative Report.


Life Events Checklist for DSM-5

Trauma exposure assessment and screening

The LEC-5 is a 17-item self-report checklist assessing exposure to stressful life events that may result in PTSD or trauma-related mental health problems. Developed by Weathers and colleagues at the National Center for PTSD in 2013, the LEC-5 identifies which types of traumatic events a person has experienced, determining the presence of a qualifying trauma exposure according to DSM-5 Criterion A. The scale is widely used in PTSD research, clinical assessment, and disaster mental health screening.

Weathers, F. W., Blake, D. D., Schnurr, P. P., Kaloupek, D. G., Marx, B. P., & Keane, T. M. (2013). The Life Events Checklist for DSM-5 (LEC-5). National Center for PTSD. Available from: www.ptsd.va.gov

Gray, M. J., Litz, B. T., Hsu, J. L., & Lombardo, T. W. (2004). Psychometric properties of the Life Events Checklist. Assessment, 11(4), 330-341.


Life History Research

life history method
life-history interview
biographical research
personal narrative research

Early 20th century (Thomas & Znaniecki 1918–1920); systematised as interview method in the 1990s

William I. Thomas and Florian Znaniecki (sociological tradition); Robert Atkinson (interview method)

Life history research is a qualitative method that captures the full arc of an individual's life — or a significant portion of it — through extended biographical interviewing and analysis of personal documents. Rooted in early Chicago School sociology, the method treats each life story as a window into broader social, cultural, and historical forces. The researcher and participant co-construct a narrative account that illuminates how personal experience is shaped by, and in turn shapes, wider social structures and processes.

In-depth biographical interviews, personal documents, diaries, letters, field notes

1–20 participants (often 1–5 for intensive biographical work)

Atkinson, R. (1998). The Life Story Interview. Sage.

Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.

narrative-analysis phenomenology ethnography grounded-theory case-study thematic-analysis


LSA

The Life-Space Assessment (LSA) is an interview-based measure developed by Baker and colleagues in 2003 to evaluate the geographic range and frequency of mobility in community-dwelling older adults. Unlike traditional measures that focus on lower extremity function in controlled settings, the LSA captures the actual areas persons frequent in daily life—from the bedroom to the neighborhood to the larger community—and the frequency and independence with which they access these spaces. It provides a comprehensive portrait of functional mobility in real-world contexts and is a strong predictor of disability, institutionalization, and mortality.

Interview-based assessment of activity range

Baker, P. S., Bodner, E. V., & Allman, R. M. (2003). Measuring life-space mobility in community-dwelling older adults. J Am Geriatr Soc, 51(11), 1610-1614.

Peel, C., Sawyer Baker, P., Roth, M., Brown, C. J., Bodner, E. V., & Allman, R. M. (2005). Assessing mobility in older adults: the UAB Study of Aging Life-Space Assessment. Phys Ther, 85(10), 1008-1119.

Haley, S. M., & Andres, P. L. (2010). Life-Space Assessment: a performance-based measure of mobility in people with chronic conditions. J Am Geriatr Soc, 58(12), 2319-2326.

short-physical-performance-battery tinetti-balance-assessment activities-balance-confidence frail-scale edmonton-frail-scale


Life Table Response Experiment

LTRE
demographic analysis
vital rate contribution
elasticity analysis

Life Table Response Experiments (LTRE) decompose observed temporal changes in population growth rate (lambda) into contributions from changes in specific vital rates (survival, reproduction). Developed by Caswell (2000) and applied extensively by Wisdom and colleagues, LTRE reveals which demographic changes drove observed population dynamics. For example, LTRE can show whether a population's decline was primarily due to reduced survival of juveniles, reduced fecundity of adults, or changes in other life stages. This guides targeted conservation or management.

Caswell, H. (2019). Sensitivity Analysis: Matrix Methods in Demography and Ecology. Springer.

Caswell, H. (2000). Matrix population models. Sinauer Associates.

Wisdom, M. J., Mills, L. S., & Doak, D. F. (2000). Life stage simulation analysis: estimating vital-rate effects on population growth for conservation. Ecology, 81(3), 628-641.

leslie-matrix integral-projection-model population-viability-analysis metabolic-theory-of-ecology


Life Table

Mortality Table
Actuarial Table
Survival Table
Yaşam Tablosu

A life table is a systematic, age-structured summary of the mortality experience of a population. It traces a hypothetical cohort of births — conventionally 100,000 — through successive age intervals, recording how many survive, how many die, and how many person-years are lived at each interval. The method was formalized in its modern probabilistic form by Chiang (1984), synthesizing centuries of actuarial and demographic practice into a rigorous statistical framework applicable to human and biological populations alike.

Input: Age-specific death and population counts

Output: Survival, mortality, and life expectancy by age interval

Chiang, C. L. (1984). The Life Table and Its Applications. Robert E. Krieger Publishing.


Lift and Gain Chart

Lift and gain charts visualize classifier performance by showing how much better the model performs compared to random selection, particularly useful for ranking or scoring tasks where you select a top percentage of samples. They are widely used in marketing, credit scoring, and fraud detection.

Maimon, O. Z., & Rokach, L. (Eds.). (2010). Data Mining and Knowledge Discovery Handbook (2nd ed.). Springer.

Naeem Siddiqi (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons.

roc-auc precision-recall-auc cumulative-gain recall


Ligand Field Analysis

Ligand Field Theory (LFT) is an advanced model of metal-ligand bonding that combines crystal field theory with molecular orbital theory. Developed systematically by Brian Norman Figgis and others from the 1960s onward, LFT provides quantitative predictions of electronic structure, magnetism, spectra, and reactivity of coordination complexes, bridging the gap between qualitative crystal field arguments and rigorous quantum mechanics.

Figgis, B. N. (1966). Introduction to Ligand Fields. Interscience Publishers.

Lever, A. B. P. (1984). Inorganic Electronic Spectroscopy (2nd ed.). Elsevier.


Light Curve Analysis

photometric analysis
transit photometry
eclipsing binary analysis

Light curve analysis is the study of the brightness variation of a celestial object over time, used to detect and characterize exoplanets, eclipsing binaries, and variable stars. When a planet transits in front of its host star, the star's brightness dips slightly. By analyzing these photometric signatures, astronomers can determine planetary radii, orbital periods, and atmospheric properties. This method has discovered thousands of exoplanets and revealed the structure of stellar systems.

Signal processing and astronomical observation technique

Ricker, G. R., et al. (2015). TESS: Transiting Exoplanet Survey Satellite. Journal of Astronomical Telescopes, Instruments, and Systems, 1(1), 014003.

Borucki, W. J., et al. (2010). Kepler Planet-Detection Mission: Introduction and First Results. Science, 327(5968), 977-980.

Mandel, K., & Agol, E. (2002). Analytic Light Curves for Planetary Transits. The Astrophysical Journal, 580(2), L171.


LightGBM

LightGBM
Light Gradient Boosting Machine
lgbm
leaf-wise gradient boosting

LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.

Gradient boosting decision tree ensemble

Classification, regression & forecasting

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

xgboost random-forest decision-tree logistic-regression isolation-forest


Lightning Jump

Lightning jump
Lightning trend
VHF lightning
Electric field analysis

The lightning jump is a rapid increase in lightning activity (number of flashes per unit time) that precedes severe weather including hail, heavy rain, and tornadoes. This phenomenon, observed using satellite or ground-based lightning detection networks, is an operational diagnostic tool for real-time severe weather warning.

Schultz, E., Petersen, W. A., & Rudlosky, S. D. (2009). Preliminary Development and Validation of the Specific Convective Activity Signature (SCAS) Product using Optical Transient Detector and Geostationary Satellite Data. Journal of Applied Meteorology and Climatology, 48(4), 642-655.

Mosier, R. M., Czarnecki, C., & Carey, L. D. (2011). Radar and Lightning Observations of the 1 August 2008 Kinston, North Carolina Supercell. Weather and Forecasting, 26(4), 459-474.


LightTS

Light Sampling-oriented MLP
LightMLP
Hafif Örnekleme Tabanlı MLP
Lightweight Time-Series MLP

LightTS is a lightweight, MLP-based architecture for multivariate time-series forecasting introduced by Tianping Zhang and colleagues in 2022. Motivated by the observation that simpler models can match or surpass heavy Transformer-based architectures, LightTS applies an interval-sampling strategy to decompose long input sequences into multiple sub-sequences and processes each with compact Chunk-MLP and Continuous-MLP modules. The design prioritizes computational efficiency while preserving both local and global temporal patterns.

Lightweight MLP-based multivariate time-series forecaster

Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., & Li, J. (2022). Less is more: Fast multivariate time series forecasting with light sampling-oriented MLP structures. arXiv preprint.


Likert Scale Construction

Rensis Likert's Method for Constructing Summated Rating Scales

Likert summated rating scale
Summated rating scale construction

Likert scale construction is a systematic methodology for developing attitude measurement instruments using summated rating scales. Introduced by Rensis Likert in 1932, it enables researchers to quantify latent constructs such as attitudes, beliefs, and psychological states by aggregating responses across multiple items. The method remains foundational to quantitative social and health sciences research.

Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55.

Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). New York: Holt, Rinehart & Winston.

DeVellis, R. F. (2016). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage Publications.

guttman-scale factor-analysis-scale content-validity-ratio confirmatory-factor-analysis-scale floor-ceiling-effect


Lilliefors Test

Lilliefors Test for Normality with Mean and Variance Unknown

Lilliefors corrected Kolmogorov-Smirnov test

The Lilliefors test is a goodness-of-fit test that checks whether a continuous sample comes from a normal (or exponential) distribution when the mean and variance are unknown and estimated from the data. Introduced by Hubert W. Lilliefors in 1967, it adjusts the critical values of the Kolmogorov-Smirnov test so that they remain valid once the distribution's parameters are estimated rather than known in advance.

Corrected Kolmogorov-Smirnov statistic with parameters estimated from the data

Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399-402.

Dallal, G. E., & Wilkinson, L. (1986). An Analytic Approximation to the Distribution of Lilliefors's Test Statistic for Normality. The American Statistician, 40(4), 294-296.

kolmogorov-smirnov-2sample shapiro-wilk-test anderson-darling-test fligner-killeen-test mood-median-test


LIME

Local Interpretable Model-agnostic Explanations (LIME)

Yerel Yorumlanabilir Model-Bağımsız Açıklamalar

Marco Ribeiro, Sameer Singh & Carlos Guestrin

LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.


Line-Intercept Sampling

Line-Intercept Sampling (LIS) is an ecological field method developed by Richard H. Canfield in 1941 for estimating vegetation cover, plant density, and structural characteristics in rangeland and forest surveys. By laying a linear transect across a study area and recording all plants intersecting the line, LIS provides efficient, unbiased estimates without requiring plots or quadrats.

Canfield, R. H. (1941). Application of the line interception method in sampling range vegetation. Journal of Forestry, 39(4), 388–394.

Warren, S. D., Conquest, L. L., & Brenkert, A. L. (1971). Cost and precision of methods for sampling shrub cover in ecological surveys. Journal of Range Management, 24(2), 141–147.

Krebs, C. J. (1998). Ecological Methodology (2nd ed.). Addison Wesley Longman.

point-intercept-sampling quadrat-sampling transect-sampling systematic-sampling


Linear Cryptanalysis

linear attack
linear approximation
piling-up lemma

Linear cryptanalysis is a known-plaintext attack that exploits linear approximations of a cipher's non-linear transformations to recover secret key bits. Introduced by Mitsuru Matsui in 1993, linear cryptanalysis provides practical attacks on ciphers like DES with computational complexity less than brute force. The technique analyzes statistical biases in how linear combinations of plaintext and ciphertext bits relate to key bits, enabling key recovery with reduced data requirements.

Matsui, M. (1993). Linear cryptanalysis method for DES cipher. In Advances in Cryptology - EUROCRYPT 1993, LNCS 765, pp. 386-397.

Matsui, M. (1994). The first experimental cryptanalysis of the Data Encryption Standard. In Advances in Cryptology - CRYPTO 1994, LNCS 839, pp. 1-11.


Linear Discriminant Analysis

Linear Discriminant Analysis (Fisher's LDA)

LDA
Fisher's discriminant analysis
Fisher linear discriminant
normal discriminant analysis
canonical discriminant analysis

Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.

Supervised dimensionality reduction and linear classifier

Classification and dimensionality reduction

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 4). Springer.

logistic-regression quadratic-discriminant-analysis principal-component-analysis support-vector-machine naive-bayes random-forest


LINEAR-MAX-NORMALIZATION

LINEAR-MAX-NORMALIZATION (Linear Max Normalization — division by column maximum (benefit) or column minimum over value (cost)) is a normalization multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fishburn, P. C. (1967). Additive Utilities with Incomplete Product Sets: Application to Priorities and Assignments. Operations Research


Linear Predictive Coding

Linear Predictive Coding for Speech Modeling and Compression

LPC
autoregressive model
speech prediction
vocal tract modeling

Linear Predictive Coding (LPC) is a powerful signal processing technique for modeling and compressing speech by assuming each speech sample can be predicted from a linear combination of previous samples. Pioneered by Burg and Makhoul in the 1970s, LPC is the foundation of speech codecs, speech synthesis, speaker recognition, and speech enhancement. LPC exploits the time-correlated structure of speech to achieve high compression ratios and enable efficient parameter extraction.

Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561–580.

Rabiner, L. R., & Schafer, R. W. (1978). Digital Processing of Speech Signals. Prentice-Hall.

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

cepstral-analysis bark-and-mel-scales psychoacoustic-masking speech-intelligibility beamforming


Linear Programming

LP
linear optimization
Doğrusal Programlama (LP)

Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences.

Mathematical programming / continuous optimization

Simplex method; interior-point methods (barrier methods)

Optimal decision-variable values and optimal objective value

Polynomial in theory (ellipsoid, interior-point); Simplex is exponential worst-case but fast in practice

Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press.

Vanderbei, R.J. (2014). Linear Programming: Foundations and Extensions. Springer.

integer-programming goal-programming nonlinear-programming stochastic-optimization network-flow


Linear Quadratic Gaussian

The Linear Quadratic Gaussian (LQG) controller combines the Linear Quadratic Regulator (LQR) with a Kalman Filter to handle stochastic systems with measurement noise and process noise. Developed by Kalman and later formalized by Athans and others, LQG is the natural stochastic extension of LQR and remains the gold standard for optimal linear control under noise, with applications spanning spacecraft, aircraft autopilot, and industrial process control.

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

Athans, M. (1971). The role and use of the stochastic linear-quadratic-gaussian problem in control system design. IEEE Transactions on Automatic Control, 16(6), 529-552.

Kwakernaak, H., & Sivan, R. (1972). Linear Optimal Control Systems. Wiley-Interscience.

linear-quadratic-regulator extended-kalman-filter kalman-filter stochastic-control


Linear Quadratic Regulator

The Linear Quadratic Regulator (LQR) is a classical optimal control algorithm that computes a linear feedback law to minimize a quadratic cost function for a linear dynamical system. Introduced by Kalman in 1960, LQR provides a provably optimal, closed-form solution for linear systems and remains fundamental in control theory, robotics, and aerospace applications because of its theoretical elegance and computational efficiency.

Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119.

Bryson, A. E., & Ho, Y. C. (1969). Applied Optimal Control: Optimization, Estimation and Control. Blaisdell Publishing.

Lewis, F. L., Vrabie, D., & Syrmos, V. L. (2012). Optimal Control (3rd ed.). John Wiley & Sons.

hamilton-jacobi-bellman-equation model-predictive-control extended-kalman-filter pontryagin-maximum-principle


Linear Regression (ML)

Linear Regression as a Machine Learning Model

ordinary least squares regression
OLS
least squares regression
multiple linear regression

Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.

Continuous numeric features and a continuous numeric target

Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer.

James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 3). Springer.

regularized-linear-regression logistic-regression-ml random-forest gradient-boosting support-vector-machine decision-tree


LINEAR-SUM-NORMALIZATION

LINEAR-SUM-NORMALIZATION (Linear Sum Normalization — column-sum division (probability / stochastic normalisation)) is a normalization multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Peldschus, F., Kaklauskas, A. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Zavadskas, E. K., Turskis, Z., Peldschus, F., Kaklauskas, A. (1994). Competitive comparison of contractors' offers in construction. Technika, Vilnius


Linguistic Acceptability Assessment

Linguistic Acceptability Assessment (Grammaticality Judgment)

Dilbilgisel Kabul Edilebilirlik Değerlendirme

1957 (theory); 2019 (neural benchmark — CoLA)

Noam Chomsky (theoretical foundations, 1957); Warstadt, Singh & Bowman (neural formulation, 2019)

Linguistic acceptability assessment is a natural-language-processing task that automatically estimates whether a sentence would be judged grammatically acceptable by a native speaker of the target language. Grounded in Chomsky's (1957) distinction between grammatical and ungrammatical utterances, the task was formalised as a neural benchmark by Warstadt, Singh and Bowman (2019) through the Corpus of Linguistic Acceptability (CoLA). It is used in language-learning research, linguistics studies, and quality auditing of natural-language-generation (NLG) systems.

NLP binary/continuous classification task

Output: Acceptability label (acceptable / unacceptable) or continuous acceptability score

Warstadt, A., Singh, A. & Bowman, S. (2019). Neural Network Acceptability Judgments. Transactions of the Association for Computational Linguistics, 7, 625–641.

Chomsky, N. (1957). Syntactic Structures. Mouton, The Hague.

sentiment-analysis text-classification bert-embeddings tf-idf


Linguistic Ethnography

Ethnographic Linguistics
Sociolinguistic Ethnography

Linguistic Ethnography is a qualitative research approach combining ethnographic fieldwork with detailed linguistic analysis to understand language use in cultural context. Developed by researchers like Ben Rampton, it examines how people use language within communities, institutions, and social interactions while paying attention to identity, power, and meaning-making. This method integrates sociolinguistics, anthropology, and discourse analysis to produce rich, contextualized understandings of language-in-society.

Rampton, B. (2007). Neo-Hymesian linguistic ethnography in the United Kingdom. Journal of Sociolinguistics, 11(5), 584-607.

Creese, A., & Blackledge, A. (2010). Translanguaging in the bilingual classroom: A pedagogy for learning and teaching? The Modern Language Journal, 94(1), 103-115.

Heath, S. B. (1983). Ways with Words: Language, Life and Work in Communities and Classrooms. Cambridge: Cambridge University Press.


Link Prediction

Link Prediction (Missing and Future Edge Inference)

Bağlantı Tahmini (Link Prediction)
missing link prediction
future link prediction
edge prediction

Link prediction is a network-analysis task that estimates which edges are missing from an observed graph or which edges are likely to form in the future. Formalised by Liben-Nowell and Kleinberg (2003, 2007), it covers a spectrum of approaches — from simple structural similarity indices such as Common Neighbors, Jaccard coefficient, and Adamic-Adar, to matrix factorisation, and graph neural network (GNN) methods — and is evaluated with AUC and Average Precision to account for the heavily imbalanced ratio of real to non-existing edges.

Structural similarity indices / Matrix factorisation / Graph Neural Networks

Output: Ranked list of candidate edges with likelihood scores

Liben-Nowell, D. & Kleinberg, J. (2007). The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.

Zhang, M. & Chen, Y. (2018). Link Prediction Based on Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 31.

graph-neural-network network-embedding community-detection centrality-analysis stochastic-block-model


Link Segment Inverse Dynamics

inverse dynamics
joint kinetics
joint moments

Inverse dynamics is a biomechanical analysis technique that calculates joint moments (forces and torques) from measured kinematics (positions and angles) and ground reaction forces. Formalized by David Winter (1990), inverse dynamics works backward from Newton's second law: given acceleration and inertia, calculate the net force (or moment) required to produce that motion. By analyzing joint loading during sport movements, biomechanists identify asymmetries, technique flaws, and muscle-group imbalances that predict injury or limit performance. Inverse dynamics is the standard for detailed biomechanical assessment in research and elite coaching.

Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. New York: John Wiley & Sons.

DeLuca, P. A. (2003). The biomechanics of walking and running. Clinical Orthopaedics and Related Research, 288, 28-51.

Yeadon, M. R., & Challis, J. H. (2010). The future of sports biomechanics. Sports Biomechanics, 9(1), 1-7.


Link Stigma Scale

Perceived Devaluation-Discrimination Scale (Link Stigma Scale)

The Link Stigma Scale, also called the Perceived Devaluation-Discrimination Scale, is a measure of perceived stigma developed by Bruce G. Link in 1987. It assesses the extent to which individuals with serious mental illness perceive that society devalues people with mental illness and discriminates against them. Unlike internalized stigma (self-directed negative beliefs), the Link Scale captures perceived external stigma—beliefs about how others view and treat people with mental illness. The scale is widely used in stigma research and mental health services to understand stigma as a social and structural phenomenon.

Link, B. G. (1987). Understanding labeling effects in the area of mental disorders: An assessment of the effects of expectations of rejection. American Sociological Review, 52(1), 96-112.

internalized-stigma-mental-illness self-stigma-seeking-help recovery-assessment-scale social-inclusion-scale


LINMAP

LINMAP (LINear programming technique for Multidimensional Analysis of Preference) is a ranking multi-criteria decision-making (MCDM) method introduced by Srinivasan, V., Shocker, A. D. in 1973. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

LP-based ideal point from pairwise preference judgements

Srinivasan, V., Shocker, A. D. (1973). Linear programming techniques for multidimensional analysis of preferences. Psychometrika

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Liposome Encapsulation

Liposomal Drug Delivery and Encapsulation

liposomal formulation
vesicular delivery
lipid nanoparticles

Liposomal encapsulation is a formulation technique using lipid bilayer vesicles (liposomes) to enclose drugs, improving bioavailability, reducing toxicity, and enabling targeted delivery. Developed by Alec Bangham in 1965, liposomes are now standard in pharmaceutical development, with several FDA-approved liposomal drugs on the market.

Bangham, A. D., Standish, M. M., & Watkins, J. C. (1965). Diffusion of univalent ions across the lamellae of swollen phospholipid films and the determination of membrane potential. Journal of Molecular Biology, 13(1), 238-252.

Torchilin, V. P. (2005). Recent advances with liposomes as pharmaceutical carriers. Nature Reviews Drug Discovery, 4(2), 145-160.


Liquidity Risk Models

Liquidity Risk Measures (Amihud, Roll, LOT)

Likidite Risk Modelleri (Amihud, Roll, LOT)

Amihud (2002); Roll (1984); Lesmond, Ogden & Trzcinka (LOT)

Liquidity Risk Models are a family of measures that quantify how easily an asset trades by capturing its price impact, its effective bid-ask spread, and a holding-period adjustment. The family brings together the Amihud illiquidity ratio (Amihud, 2002), the Roll serial-covariance spread estimator (Roll, 1984), and the LOT (Lesmond-Ogden-Trzcinka) realised-spread measure.

Liquidity / illiquidity measurement models

Volume-return ratio (Amihud), serial covariance (Roll), realised spread (LOT)

Amihud, Y. (2002). Illiquidity and Stock Returns: Cross-Section and Time-Series Effects. Journal of Financial Markets, 5(1), 31-56.

Roll, R. (1984). A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market. Journal of Finance, 39(4), 1127-1139.

har-rv-model jump-diffusion-model pairs-trading ols-regression risk-parity-model


LISA

Local Indicators of Spatial Association (Local Moran's I)

LISA — Yerel Uzamsal Otokorelasyon (Local Moran's I)

LISA, introduced by Luc Anselin in 1995, is a local statistic that computes spatial autocorrelation separately for every observation rather than for the map as a whole. It pinpoints where high or low values cluster and where spatial outliers sit, decomposing the global Moran's I into a contribution from each location.

Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115.

morans-i-test spatial-lag-model spatial-error-model spatial-durbin-model kriging-interpolation


Narrative Literature Review

Narrative Literature Review (Interpretive Evidence Synthesis Without Systematic Search)

narrative review
literature survey
interpretive review
state-of-the-art review

Research community (traditional academic writing format)

A narrative literature review is an interpretive synthesis of published research organized around themes, concepts, or historical progression rather than systematic search. Unlike systematic reviews, narrative reviews employ subjective study selection, do not require protocol registration, and prioritize depth of interpretation over exhaustive comprehensiveness. Narrative reviews are valuable for conceptual synthesis, exploring emerging fields with sparse literature, and providing historical context; they have been the traditional form of scholarly literature synthesis since the inception of academic journals.

Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of Chiropractic Medicine, 5(3), 101–117.

Cronin, P., Ryan, F., & Coughlan, M. (2008). Undertaking a literature review: a step-by-step approach. British Journal of Nursing, 17(1), 38–43.

Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.

systematic-review-article meta-analysis-article original-research-article literature-search-strategy


Little's Law

L = λW Theorem
Little's Theorem
Little's Result
Little Yasası

Little's Law is a fundamental theorem in queueing theory that relates the long-run average number of items in a stable system (L) to the long-run average arrival rate (λ) and the long-run average time an item spends in the system (W), expressed as L = λW. Introduced and rigorously proved by John D. C. Little in 1961, the law holds for virtually any stable stochastic system, requiring no assumptions about arrival distributions, service distributions, or queue disciplines.

Little, J. D. C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383–387.


Live/Dead Assay

Live/Dead Cell Viability Fluorescence Assay

calcein-AM/propidium iodide
SYTO/PI staining
fluorescent viability stain

The Live/Dead assay is a fluorescence-based method for simultaneously identifying live and dead cells using two complementary dyes. The assay combines calcein-AM (or SYTO fluorophores), which generates bright green fluorescence in living cells with intact esterase activity, with propidium iodide (PI), which produces red fluorescence in dead cells with compromised membrane integrity. Commercially developed by Molecular Probes and now part of Thermo Fisher's portfolio, the Live/Dead kit is widely used to evaluate cell viability on biomaterial scaffolds, in tissue constructs, and following drug or toxin exposure.

Molecular Probes (2004). LIVE/DEAD Viability/Cytotoxicity Kit user guide. Invitrogen Corporation.

Niles, A. L., Moravec, R. A., & Riss, T. L. (2009). In vitro viability and cytotoxicity testing and same-well multiplexing assays for adherent cells. Current Chemical Genomics, 3, 33-43.

Riss, T. L., Moravec, R. A., Niles, A. L., et al. (2011). Cell viability assays. In Assay Guidance Manual (3rd ed.). Eli Lilly & Company and the National Center for Advancing Translational Sciences.

mtt-mts-assay hemolysis-assay cam-assay scratch-wound-assay


Living Systematic Review

Living Systematic Review (Continuously Updated Synthesis)

LSR
Continually Updated Review
Dynamic Evidence Synthesis

Elliott et al. (2017), Advanced by Cochrane Collaboration

A living systematic review (LSR) is a dynamic, continuously updated evidence synthesis that monitors emerging literature and incorporates new studies as they become available, rather than being a static document published once. Formalized by Elliott et al. (2017) and adopted by the Cochrane Collaboration, living systematic reviews maintain currency in rapidly evolving fields by using prospective searches and regular review cycles (monthly, quarterly, or trigger-based). Rather than waiting 12-18 months for a complete systematic review only to find it outdated by new trials, living reviews enable real-time evidence synthesis—particularly valuable during pandemics, in rapidly advancing fields (oncology, immunology), and for volatile policy questions where new evidence frequently shifts recommendations.

Elliott, J. H., Synnot, A., Turner, T., Simmonds, M., Akl, E. A., McDonald, S., ... Higgins, P. T. (2017). Living systematic reviews: An emerging opportunity to narrow the evidence-practice gap. PLOS Medicine, 14(2), e1002254.

Higgins, P. T., & Thomas, J. (Eds.). (2021). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.2). The Cochrane Collaboration.

Shea, B. J., Reeves, B. C., Wells, G., et al. (2017). AMSTAR 2: a critical appraisal tool for systematic reviews. BMJ, 358, j4008.

systematic-review rapid-review-methodology evidence-synthesis-framework meta-analysis


Ljung-Box Test

Ljung-Box Q Test
Modified Box-Pierce Test
Portmanteau Test for Autocorrelation
Otokorelasyon Portmanteau Testi

The Ljung-Box Q test is a diagnostic portmanteau test proposed by Ljung and Box (1978) to assess whether a group of autocorrelations in a time series residual sequence is jointly zero. It is widely used to evaluate the adequacy of fitted time series models — especially ARIMA models — by testing whether remaining residuals exhibit any systematic pattern. The test is applicable in econometrics, finance, and any field that relies on temporal data modeling.

Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303.


LMAW

LMAW (Logarithm Methodology of Additive Weights) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Žižović, M., Biswas, S., Božanić, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pamučar, D., Žižović, M., Biswas, S., Božanić, D. (2021). A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics. Facta Universitatis, Series: Mechanical Engineering


LMDI Decomposition

Logarithmic Mean Divisia Index
LMDI-I Additive Decomposition
LMDI-II Multiplicative Decomposition
Logaritmik Ortalama Divisia İndeksi

Log-Mean Divisia Index (LMDI) Decomposition is a quantitative technique for attributing changes in an aggregate indicator — most commonly energy consumption or CO₂ emissions — to its underlying driving factors, such as activity level, structural mix, and intensity. Introduced in its definitive practical form by B. W. Ang in 2005, LMDI builds on Divisia index theory and uses the logarithmic mean as a weighting function to achieve a mathematically perfect, residual-free decomposition.

Additive (LMDI-I) and multiplicative (LMDI-II)

Ang, B. W. (2005). The LMDI approach to decomposition analysis: a practical guide. Energy Policy, 33(7), 867–871.


Load Forecasting

Electrical Load Forecasting and Demand Prediction

demand forecasting
electricity consumption prediction
load demand estimation

Load forecasting predicts future electrical demand on power systems across various time horizons: minutes to hours (short-term), days to weeks (medium-term), and months to years (long-term). Accurate forecasting is essential for economic dispatch, unit commitment, and system reliability. Methods range from classical statistical regression to modern machine learning approaches.

Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16(1), 44-55.

Charlton, J. D., Kalamara, E., & James, R. D. (2008). Quantifying electricity load profiles and demand patterns. Energy Policy, 36(1), 181-193.

Bunn, D. W. (2005). Forecasting with Multiple Models: A Case Study of Electric Load Forecasting. Futures, 37(8), 896-906.

power-flow-analysis energy-storage-dispatch smart-grid-state-estimation harmonic-distortion-analysis


Load-Pull

Load-Pull Measurement and Optimization of RF Power Amplifiers

Load-pull measurement
Source-pull optimization

RF/microwave measurement and optimization

Load-Pull is an experimental technique for characterizing and optimizing RF power amplifier performance under varying load and source impedance conditions. Introduced by Davidson et al. in 1990, load-pull measurements vary the load impedance seen by the amplifier while recording output power, efficiency, and linearity. Load-pull reveals contours of constant gain, efficiency, and stability, enabling optimal impedance matching for maximum performance. Essential for power amplifier design and characterization.

Experimental characterization of power amplifier performance under varying load/source conditions

Cripps, S. C. (1999). RF Power Amplifiers for Wireless Communications. Artech House.

Davidson, A., Strahler, L., Kim, S., Tajalli, A., & Komiak, J. (1990). Broad-band load-pull characterization of power devices for microwave and millimeter-wave applications. IEEE Transactions on Microwave Theory and Techniques, 38(12), 1779-1786.

Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (2005). Discrete-time signal processing. Prentice Hall.


Local Average Treatment Effect

Local Average Treatment Effect (LATE / Complier Average Causal Effect)

Yerel Ortalama Tedavi Etkisi (LATE / CACE)

Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)

The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.

Two-stage least squares (2SLS / Wald ratio)

Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.

Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455.

iv-2sls propensity-score-matching heterogeneous-treatment-effects frontdoor-adjustment regression-discontinuity


Local Geary's C

Local Geary
local spatial contiguity ratio
LISA Geary
local c statistic

Local Geary's C is a local indicator of spatial association (LISA) that measures, for each location, how dissimilar its value is from its immediate neighbours. Unlike Local Moran's I, which detects clustering of similar values, Local Geary's C focuses on squared value differences and is especially sensitive to local spatial outliers and local heterogeneity.

Georeferenced areal or point data with a defined spatial weights matrix

Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.

Anselin, L. (2019). A local indicator of multivariate spatial association: extending Geary's C. Geographical Analysis, 51(2), 133–150.

local-morans-i gearys-c local-indicators-of-spatial-association local-getis-ord-gi-star spatial-autocorrelation morans-i


GWR
geographically weighted regression
local spatial regression
spatially varying coefficient model

Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data.

Spatially varying coefficient regression

Georeferenced (point or areal) data with continuous outcome

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

Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281-298.


Local Getis-Ord Gi*

Gi* statistic
Getis-Ord Gi*
local G-star
hot spot statistic

The Local Getis-Ord Gi* statistic identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots) within a study area. Unlike global measures, it produces a z-score for every location, revealing where concentrated clustering occurs and with what statistical confidence.

Georeferenced continuous or count data on a spatial lattice or point pattern

Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.

Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306.

local-morans-i local-indicators-of-spatial-association hot-spot-analysis spatial-autocorrelation kernel-density-estimation gearys-c


Local Hot Spot Analysis

local Getis-Ord Gi*
Gi* statistic
spatial hot spot detection
local spatial clustering

Local Hot Spot Analysis uses the Getis-Ord Gi* statistic to identify specific geographic locations where high or low values cluster together more than expected by chance. Unlike global measures that return a single summary for the whole study area, this local statistic produces a z-score for each feature, pinpointing exactly where statistically significant hot spots and cold spots occur.

Georeferenced point or areal data with continuous attribute values

Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.

Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.

local-morans-i local-spatial-autocorrelation local-getis-ord-gi-star kernel-density-estimation hot-spot-analysis local-indicators-of-spatial-association


Local Indicators of Spatial Association

Local Indicators of Spatial Association (LISA)

local spatial autocorrelation statistics

LISA, introduced by Luc Anselin in 1995, decomposes a global spatial autocorrelation index into a location-specific statistic for every observation. It identifies where statistically significant spatial clusters and outliers occur on a map, enabling researchers to move beyond a single global summary and pinpoint the geographic sources of spatial dependence.

Georeferenced areal / point data with a continuous or count attribute

Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115.

Anselin, L. (2010). Local Spatial Autocorrelation. In A. S. Fotheringham & P. A. Rogerson (Eds.), The SAGE Handbook of Spatial Analysis (pp. 255–275). SAGE Publications.

morans-i gearys-c hot-spot-analysis spatial-autocorrelation local-getis-ord-gi-star geographically-weighted-regression


Local Kernel Density Estimation

spatially adaptive kernel density estimation

Local Kernel Density Estimation (Local KDE) is a non-parametric spatial method that estimates the density of point events at each location by applying a kernel function with a spatially adaptive bandwidth. Unlike global KDE, which uses a fixed bandwidth across the entire study area, Local KDE adjusts the smoothing window according to local data density, capturing fine-scale clustering where events are sparse or concentrated.

Point event data, georeferenced observations

Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.

Diggle, P. J. (1985). A kernel method for smoothing point process data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 34(2), 138-147.

kernel-density-estimation hot-spot-analysis local-spatial-autocorrelation local-morans-i spatial-autocorrelation network-based-spatial-analysis


Local Kriging

moving-window kriging
local kriging interpolation
windowed kriging
neighborhood kriging

Local Kriging is a spatially adaptive geostatistical interpolation method that restricts each prediction to a moving neighborhood of nearby observations, fitting a variogram model locally within that window. This allows spatial covariance structure to vary across the study region rather than imposing a single global variogram, making it better suited to large or non-stationary spatial fields.

Georeferenced continuous measurements (point data)

Haas, T. C. (1990). Kriging and automated variogram modeling within a moving window. Atmospheric Environment, 24(7), 1759-1769.

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.

ordinary-kriging kriging geographically-weighted-regression kernel-density-estimation co-kriging multiscale-kriging


Local Moran's I

Local Indicator of Spatial Association
LISA statistic
Anselin Local Moran
local spatial autocorrelation index

Local Moran's I, introduced by Luc Anselin in 1995, is a Local Indicator of Spatial Association (LISA) that decomposes global spatial autocorrelation into location-specific contributions. For every observation it produces a signed statistic and a significance value, enabling researchers to identify spatial clusters (high-high, low-low) and spatial outliers (high-low, low-high) on a map.

Georeferenced areal or point data with a spatial weights matrix

Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.

Anselin, L. (2010). Local spatial autocorrelation. In A. S. Fotheringham & P. A. Rogerson (Eds.), The SAGE Handbook of Spatial Analysis (pp. 255–278). SAGE Publications.

morans-i gearys-c local-getis-ord-gi-star local-indicators-of-spatial-association spatial-autocorrelation hot-spot-analysis


Local Network-Based Spatial Analysis

local network analysis
local spatial network analysis
neighborhood network analysis
local graph-based spatial analysis

Okabe, Sugihara, and spatial network analysis community

Local Network-Based Spatial Analysis computes spatial statistics and network measures — such as accessibility, centrality, and density — within restricted local neighborhoods of a spatial network, revealing how connectivity and flow vary across fine geographic scales rather than globally across the entire network.

Network geometry, node/edge attributes, spatial coordinates

Okabe, A., & Sugihara, K. (2012). Spatial Analysis Along Networks: Statistical and Computational Methods. Wiley.

Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: A primal approach. Environment and Planning B: Planning and Design, 33(5), 705–725.

network-based-spatial-analysis kernel-density-estimation hot-spot-analysis local-spatial-autocorrelation geographically-weighted-regression local-getis-ord-gi-star


Local Ordinary Kriging

Local Ordinary Kriging (Moving Window Kriging)

moving window kriging
local kriging
neighborhood kriging
LOK

Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner

Local Ordinary Kriging (LOK) is a geostatistical interpolation method that estimates values at unsampled locations using only a spatially defined moving neighborhood of nearby observations. By restricting each prediction to a local data window rather than the full dataset, LOK accommodates spatial non-stationarity, reduces computational cost, and often yields more accurate local predictions than global ordinary kriging.

Geostatistical interpolation (local/moving-window variant)

Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley.


Local Outlier Factor

Local Outlier Factor (LOF): Density-Based Anomaly Detection

LOF
local outlier factor
density-based outlier detection
local density deviation

Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.

Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space.

Density-based anomaly detection (unsupervised)

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104.

Aggarwal, C. C. (2017). Outlier Analysis (2nd ed., Ch. 4). Springer.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 14). Springer.

isolation-forest one-class-svm dbscan k-nearest-neighbors autoencoder


LOCAL-OWA

LOCAL-OWA (neighbourhood-adaptive Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J.; Liu, X. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Range-sensitive neighbourhood-local OWA — criterion weights w^q_k scale with local criterion variance within each spatial neighbourhood; order weights λ_k remain global, encoding a single risk attitude applied everywhere

Malczewski, J., Liu, X. (2014). Local ordered weighted averaging in GIS-based multicriteria analysis. Annals of GIS

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