Getis-Ord Gi*

Getis-Ord Gi* (Sıcak/Soğuk Nokta Analizi)

Getis-Ord Gi* is a local spatial statistic, introduced by Getis and Ord in 1992 and refined in 1995, that compares the value at each location and its neighbours against the global mean to identify statistically significant clusters of high values (hot spots) and low values (cold spots).

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. Geographical Analysis, 27(4), 286–306.

morans-i local-morans-i lisa geary-c spatial-lag-model


General Health Questionnaire

Psychological distress and mental well-being screening

The General Health Questionnaire-12 (GHQ-12) is a brief, 12-item self-report screening instrument for psychological distress and mental health problems in the general population. Developed by David P. Goldberg, the GHQ-12 is the most widely used short form of the longer General Health Questionnaire series. It is designed for rapid detection of minor psychiatric morbidity and assessment of psychological well-being in clinical, occupational health, and community settings.

Goldberg, D. P. (1972). The detection of psychiatric illness by questionnaire. Oxford University Press.

Goldberg, D. P., & Williams, P. (1992). A user's guide to the General Health Questionnaire. Windsor: NFER-Nelson.


Giacomini-White Test

Giacomini-White Test of Conditional Predictive Ability

GW Test
Conditional Predictive Ability Test
Giacomini-White CPA Test
Koşullu Tahmin Yeteneği Testi

The Giacomini-White (GW) test, introduced by Raffaella Giacomini and Halbert White in 2006, evaluates whether two competing forecasting methods have equal conditional predictive ability given information available at the time of forecast. Unlike unconditional tests such as the Diebold-Mariano test, it asks whether one method systematically outperforms the other in specific economic or market conditions, making it especially useful for practitioners who need state-dependent forecast comparisons.

Equal conditional predictive ability of two forecasting methods

Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.


Gibbs Sampling for Model Comparison

Gibbs Sampling for Bayesian Model Comparison

Bayesian model comparison with Gibbs sampling

Gibbs sampling for model comparison is a Bayesian MCMC approach that simultaneously samples from the space of competing models and their parameters. By augmenting the Gibbs sampler with a discrete model-index variable, posterior model probabilities and Bayes factors are estimated from the resulting Markov chain without requiring separate runs per model.

continuous, discrete, or mixed; any data suitable for the competing models

Carlin, B. P. & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 57(3), 473-484.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.


Gibbs Sampling with Measurement Error

Gibbs Sampling for Models with Measurement Error

Gibbs sampler with errors-in-variables
MCMC measurement error model
Bayesian errors-in-variables Gibbs
Gibbs EIV sampling

Gelfand & Smith (Gibbs sampler); Richardson & Gilks (measurement error extension)

Gibbs sampling with measurement error is a Bayesian MCMC method that jointly estimates unknown true covariate values and model parameters when the observed data are corrupted by measurement error. By treating the latent true values as additional unknowns, it samples all quantities iteratively from their full conditional distributions, propagating measurement uncertainty into every downstream inference.

Continuous or categorical observations subject to measurement error; hierarchical or latent-variable data

Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398–409.

Richardson, S. & Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology, 138(6), 430–442.


Gibbs Sampling with Missing Data

Gibbs Sampling with Missing Data Imputation

data augmentation Gibbs sampler
Gibbs sampler with data augmentation
Bayesian imputation via Gibbs sampling
MCMC missing data imputation

Tanner & Wong (data augmentation), Gelfand & Smith (Gibbs sampler)

Gibbs sampling with missing data treats unobserved values as additional unknowns alongside model parameters and samples all of them jointly within a Markov chain Monte Carlo loop. The method alternates between drawing the missing values from their conditional distribution given the parameters and drawing the parameters from their conditional distribution given the completed data, producing a posterior over both simultaneously.

any data with partially observed variables

Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528–540.

Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley.


Gibbs Sampling

Gibbs sampler
coordinate-wise MCMC
systematic scan Gibbs
blocked Gibbs sampling

Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form.

any data with a tractable posterior conditional structure

Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.

Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398-409.

mcmc metropolis-hastings hamiltonian-monte-carlo bayesian-regression hierarchical-bayesian-inference variational-inference


Gingival Index

Gingival Index for Periodontal Health Assessment

The Gingival Index (GI), also known as the Loe-Silness Index, is a standardized clinician-rated assessment tool for measuring the severity of gingival inflammation and periodontal disease. Developed by Loe and Silness in 1963, the GI remains the gold standard for quantifying gum inflammation in clinical research and practice. It grades inflammation on a 0-3 ordinal scale based on visual appearance and bleeding response, enabling objective assessment of periodontal health, monitoring of periodontal disease progression, and evaluation of treatment efficacy.

Löe, H., & Silness, J. (1963). Periodontal disease in pregnancy. I. Prevalence and severity. Acta Odontologica Scandinavica, 21(6), 533-551.


GINI-WEIGHT

GINI-WEIGHT (Gini Coefficient Weighting — inequality-of-discrimination objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Gini, C. in 1912. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Objective (Gini inequality coefficient applied to normalised criterion column)

Gini, C. (1912). Variabilità e mutabilità. Studi economico-giuridici della R. Università di Cagliari

ahpsort aploco aras aroman artasi cobra cocoso codas


GIS-MCDA

GIS-Based Multi-Criteria Decision Analysis

GIS-MCDM
spatial multi-criteria analysis
GIS-AHP
weighted overlay suitability
CBS tabanlı çok kriterli karar analizi

GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data.

Spatial multi-criteria suitability/decision analysis

Output: Suitability / priority map

Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.

Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill.

ahp topsis location-allocation least-cost-path


GJR-GARCH

GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)

Glosten, Jagannathan & Runkle (1993); Zakoian (1994)

GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).

Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.

Zakoian, J. M. (1994). Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18(5), 931-955.

arch-model garch-model egarch arima tbats


GBI

Karol Robinson, Sophia Gatehouse, and Gordon G. Browning

The Glasgow Benefit Inventory (GBI) is an 18-item self-report questionnaire designed to measure change in health status and general well-being resulting from otolaryngologic intervention (surgery, medical treatment). Unlike generic health-related quality-of-life measures, the GBI is disease-specific, asking patients to compare their post-intervention status to their pre-intervention baseline. Developed by Robinson, Gatehouse, and Browning in 1996, the GBI has become the standard outcome measure for evaluating benefit from ear, nose, and throat surgery and treatment.

Robinson, K., Gatehouse, S., & Browning, G. G. (1996). Measuring patient benefit from otorhinolaryngological surgery and treatment. Annals of Otology, Rhinology & Laryngology, 105(6), 415-422.


Glasgow-Blatchford Score

Glasgow-Blatchford Score for Upper GI Bleeding Risk

The Glasgow-Blatchford score (GBS), developed by Blatchford et al. in 2000, is a 23-point risk stratification tool for predicting the need for intervention (transfusion, endoscopic therapy, surgery) in patients presenting with acute upper gastrointestinal bleeding. It integrates clinical and laboratory data to identify low-risk patients who may be candidates for outpatient or non-interventional management.

Gastrointestinal bleeding risk stratification

Blatchford, O., Murray, W. R., & Blatchford, M. (2000). A risk score to predict need for treatment for upper-gastrointestinal haemorrhage. Lancet, 356(9238), 1318-1321.

Stanley, A. J., Laine, L., & Dalton, H. R. (2009). Management of acute upper and lower gastrointestinal bleeding. Gut, 58(11), 1407-1417.


Glasgow Coma Scale

Glasgow Coma Scale (GCS) for Consciousness Assessment

The Glasgow Coma Scale (GCS), developed by Teasdale and Jennett in 1974, is a 15-point scale used to assess level of consciousness and severity of brain injury. It evaluates eye opening, verbal response, and motor response, making it the gold standard tool for rapid neurological assessment in trauma, emergency, and intensive care settings.

Consciousness and neurological assessment

Teasdale, G., & Jennett, B. (1974). Assessment of coma and impaired consciousness. A practical scale. Lancet, 2(7872), 81-84.

Teasdale, G., Maas, A. I. R., Lecky, F., Manley, G., Stocchetti, N., & Murray, G. (2014). The Glasgow Coma Scale at 40 years: standing the test of time. Lancet Neurology, 13(8), 844-854.


GSES

Performance effort in sleep; cognitive effort

The Glasgow Sleep Effort Scale (GSES) is a brief instrument designed to measure the degree of mental and behavioral effort exerted in attempting to fall asleep. Developed by Broomfield and Espie in 2005, the GSES captures a key cognitive-behavioral maintenance mechanism in insomnia: excessive effort to sleep, anxiety about sleep performance, and counterproductive behaviors (trying hard to fall asleep, monitoring sleep, checking the clock) that paradoxically perpetuate sleep difficulty. The GSES is increasingly recognized as an important outcome measure for cognitive-behavioral therapy for insomnia (CBT-I).

Broomfield, N. M., & Espie, C. A. (2005). Initial insomnia severity index scores in primary care strongly predict outcome after cognitive behavioral therapy for insomnia. Journal of Clinical Psychiatry, 66(11), 1409-1415.


The GQL-15 is a 15-item, disease-specific quality of life questionnaire designed to measure the impact of glaucoma on patients' daily functioning and psychological well-being. Developed by Nelson, Aspinall, and colleagues in the UK (1998), the GQL-15 emphasizes glaucoma-specific concerns—visual field loss, peripheral vision, lighting sensitivity, and falls risk—making it a sensitive complement to generic or broader vision-related instruments in glaucoma populations.

Nelson, P., Aspinall, P. A., Papasthathis, K., et al. (1998). Quality of life in glaucoma and its relationship with visual function. J Glaucoma, 7(2), 71-78.

Nelson, P., Aspinall, P. A., O'Brien, C. (1998). Screening for glaucoma with the Glaucoma-15. Br J Ophthalmol, 82(4), 327-334.

nei-vfq-25 visual-function-index low-vision-quality-of-life impact-vision-impairment


Physician Global Assessment of Lupus Activity

Physician Global Assessment
PGA-Lupus
Clinician Global Assessment-SLE

The Physician Global Assessment (PGA) is a clinician-rated, single-item measure of overall systemic lupus erythematosus (SLE) disease activity on a visual analogue scale (0–10). Used alongside structured indices like SLEDAI, PGA captures the clinician's integrated judgment of SLE severity, synthesising clinical examination, serology, imaging, and organ-specific findings into a holistic activity score. PGA is simple, practical, and widely used in SLE research and clinical practice as a complementary measure that reflects experienced clinician assessment of disease state.

Petri M. Thermodynamic instability in the pathogenesis of lupus nephritis. Nat Rev Rheum. 2016;12(11):635-642.

Bombardier C, Gladman DD, Urowitz MB, Caron D, Chang CH. Derivation of the SLEDAI: a disease activity index for lupus patients. Arthritis & Rheumatism. 1992;35(6):630-640.

sledai das28 sdai-rheumatoid-arthritis cdai-rheumatoid-arthritis basdai


Global Co-Kriging

global cokriging
co-kriging
cokriging
multivariate kriging

Matheron (geostatistics framework); formalized for multivariate case by Myers (1982)

Global Co-Kriging is a multivariate geostatistical interpolation method that estimates an unsampled primary variable by exploiting its spatial cross-correlation with one or more secondary variables. Unlike local (moving-window) approaches, it fits a single set of variogram and cross-variogram models to the entire study domain and solves one global cokriging system for each prediction location.

Georeferenced continuous primary and secondary variables

Myers, D. E. (1982). Matrix formulation of co-kriging. Journal of the International Association for Mathematical Geology, 14(3), 249–257.

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

ordinary-kriging co-kriging universal-kriging kriging spatial-autocorrelation multiscale-co-kriging


Global Getis-Ord Gi*

Global G statistic
Getis-Ord General G
General G*
Global spatial clustering statistic

The Global Getis-Ord Gi* statistic measures the overall degree of spatial clustering of high or low values across an entire study region. It answers whether the study area, taken as a whole, exhibits significant concentration of high values (hot clustering) or low values (cold clustering), returning a single summary Z-score for the entire dataset.

Georeferenced continuous attribute data with spatial weights

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


Global Hot Spot Analysis

Global Hot Spot Analysis (Getis-Ord G Statistic)

Global G statistic
Getis-Ord G
global spatial clustering test
global concentration statistic

Global Hot Spot Analysis uses the Getis-Ord G statistic to determine whether high or low attribute values are spatially concentrated across an entire study area. It answers one question: is there overall clustering of high values (a hot spot tendency) or low values (a cold spot tendency) in the dataset as a whole, producing a single summary test for the full region.

Georeferenced point or areal data with a continuous attribute value

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

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


Global Kriging

Global Kriging (Global-Neighborhood Ordinary Kriging)

global-neighborhood kriging
full-data kriging
exhaustive kriging
non-local kriging

Georges Matheron (kriging framework); global neighborhood usage formalized in applied geostatistics

Global Kriging is the ordinary kriging interpolation procedure applied using all available sample points as the neighborhood — no spatial search window limits which data contribute to each prediction. It produces optimal linear unbiased predictions of an unobserved value at any target location, with associated prediction-error variances, by exploiting a fitted variogram model that encodes spatial autocorrelation across the entire dataset.

Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience.

Isaaks, E. H., & Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford University Press.

ordinary-kriging local-kriging universal-kriging co-kriging spatial-autocorrelation kriging


Global Moran's I

Global Moran's I Spatial Autocorrelation Statistic

Moran's I
global spatial autocorrelation index
Moran index
GMI

Global Moran's I is the most widely used single-number summary of spatial autocorrelation across an entire study area. It compares the attribute value at each location with values at neighbouring locations using a spatial weights matrix, and returns a statistic ranging from −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering). A significance test determines whether the observed pattern is stronger than random chance.

Global spatial autocorrelation test / index

Areal or point data with a spatial weights matrix

Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.

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

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


Global Ordinary Kriging

ordinary kriging
OK
global kriging
stationary ordinary kriging

Danie G. Krige; formalized by Georges Matheron

Global Ordinary Kriging (GOK) is the canonical geostatistical interpolation method that estimates values at unsampled locations as a weighted linear combination of nearby observations. It fits a single variogram model to the entire dataset, enforcing a global stationarity assumption, and produces optimal unbiased predictions along with quantified prediction uncertainty at every interpolated point.

Continuous spatial point data (georeferenced measurements)

Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley.

Chiles, J.-P., & Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty (2nd ed.). Wiley.

ordinary-kriging universal-kriging co-kriging local-ordinary-kriging spatial-autocorrelation kernel-density-estimation


Global Rating of Change Scale

Ruben Jaeschke, Jack Singer, Gordon H. Guyatt

The Global Rating of Change (GRC) Scale is a single-item, self-report outcome measure that asks patients to rate the overall change in their condition since baseline assessment. Developed by Jaeschke, Singer, and Guyatt in 1989 and published in Controlled Clinical Trials, the GRC Scale has become a fundamental method for anchor-based interpretation of change scores on clinical outcome measures, enabling clinicians and researchers to determine the minimal clinically important difference (MCID) in standardized scales.

Jaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials. 1989;10(4):407-415.

patient-specific-functional-scale lower-extremity-functional-scale ikdc-subjective-knee-form faos


Global Remote Sensing Classification

Global Remote Sensing Image Classification

wall-to-wall remote sensing classification

1970s–1980s (pixel-based global classifiers); global land-cover products 1990s–2000s

Rosenfeld & Kak; Jensen; Campbell & Wynne (textbook codifications)

Global Remote Sensing Classification assigns every pixel across an entire image or worldwide dataset to a discrete land-cover or thematic class. Treating the scene uniformly — rather than adapting to local subregions — this wall-to-wall approach underpins continental and global land-cover products such as GlobCover, FROM-GLC, and ESA CCI Land Cover.

Supervised / unsupervised image classification

Multi-spectral or hyperspectral raster imagery (satellite or airborne)

Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing (5th ed.). Guilford Press.

Turner, W., Rondinini, C., Pettorelli, N., Mora, B., Leidner, A. K., Szantoi, Z., ... & Woodcock, C. (2015). Free and open-access satellite data are key to biodiversity conservation. Biological Conservation, 182, 173-176.


Global Sensitivity Analysis

Global Sensitivity Analysis (Sobol, Morris, FAST)

Global Duyarlılık Analizi (Sobol, Morris, FAST)

I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)

Global sensitivity analysis (GSA) is a family of techniques that decompose the variance of a model's output across its input parameters, quantifying how much each input — and each combination of inputs — contributes to the total uncertainty in the result. Sobol's variance-based indices (2001), Morris's one-at-a-time (OAT) screening (1991), and the Fourier Amplitude Sensitivity Test (FAST, first proposed by Cukier et al. in 1973) are the three most widely used approaches. Together they serve as the standard toolkit for identifying which parameters drive model behaviour and which can be safely fixed.

Sobol variance decomposition / Morris OAT screening / FAST Fourier-based decomposition

Output: First-order (S1) and total-effect (ST) sensitivity indices per input parameter

Sobol, I.M. (2001). Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280.

Saltelli, A. et al. (2008). Global Sensitivity Analysis: The Primer. Wiley.

uncertainty-quantification monte-carlo-simulation latin-hypercube-sampling design-of-experiments surrogate-modelling


Global Spatial Autocorrelation

global spatial dependence
global Moran's I
GSA
global spatial clustering measure

P. A. P. Moran (Moran's I, 1950); generalized by Luc Anselin

Global Spatial Autocorrelation measures the degree to which similar values cluster together across an entire study area. Rather than identifying where clusters occur, it yields a single summary statistic — most commonly Moran's I — that quantifies whether spatial proximity coincides with value similarity, dissimilarity, or randomness across all observations simultaneously.

Georeferenced areal or point data with a spatial weights matrix

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.

morans-i gearys-c local-spatial-autocorrelation local-morans-i spatial-autocorrelation hot-spot-analysis


Global Spatial Durbin Model

spatially lagged X model with spatial lag

Durbin (1960); adapted to spatial context by LeSage & Pace (2009)

The Global Spatial Durbin Model extends the spatial lag model by including not only a spatially lagged dependent variable but also spatially lagged independent variables (WX). A single set of global coefficients applies uniformly across all locations, making it suitable for estimating average spillover effects when spatial dependence is pervasive throughout the study region.

Cross-sectional or panel geo-referenced data with a spatial weights matrix

LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.

Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.


Global Spatial Error Model

SEM
spatial error model
spatial error regression
global SEM

The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations.

Cross-sectional or pooled spatial data with continuous outcome

Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In A. Ullah & D. E. A. Giles (Eds.), Handbook of Applied Economic Statistics (pp. 237-289). Marcel Dekker.

global-spatial-lag-model global-spatial-durbin-model spatial-autocorrelation morans-i geographically-weighted-regression ols-regression


Global Spatial Panel Model

spatial panel model with global weights
global spatial panel regression
spatial panel data model
GSPM

The Global Spatial Panel Model extends panel data regression by incorporating a global spatial weights matrix that links every location to every other location simultaneously. It jointly accounts for cross-sectional spatial dependence, time-series dynamics, and individual fixed or random effects, making it the standard workhorse for panel data when spatial spillovers operate across the full study region.

Areal / lattice panel data with spatial weights

Lee, L. F., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165-185.

spatial-lag-model spatial-error-model spatial-durbin-model geographically-weighted-regression panel-spatial-lag-model global-spatial-lag-model


Global Universal Kriging

universal kriging (global)
global UK
kriging with external drift (global)
global trend kriging

Global Universal Kriging is a geostatistical interpolation method that models a spatially varying trend (drift) as a deterministic function of coordinates and uses the entire dataset to fit both the trend coefficients and the residual variogram simultaneously. It produces optimal linear unbiased predictions together with pointwise estimation uncertainty, accounting for a large-scale spatial gradient across the full study region.

Continuous georeferenced point observations

Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London.

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

ordinary-kriging universal-kriging co-kriging local-universal-kriging kriging multiscale-kriging


Global VAR

Global VAR (GVAR) is a large-scale macroeconomic modeling framework linking multiple countries (or regions) via trade and financial channels, allowing shocks in one country to propagate through the global system. Introduced by Pesaran et al. (2004), it solves the curse of dimensionality in international VAR models by estimating country-specific VARs conditional on foreign variables, then solving a system linking all countries. This approach is invaluable for analyzing global spillovers and international policy coordination.

Pesaran, M. H., Schuermann, T., & Weiner, S. M. (2004). Modeling regional interdependencies using a global error-correcting macroeconometric model. Journal of Business and Economic Statistics, 22(2), 129-162.

Chudik, A., & Pesaran, M. H. (2016). Theory and practice of GVAR modelling. Journal of Economic Surveys, 30(2), 165-197.


Glottochronology

Glottochronology, or lexicostatistics, is a quantitative method in historical linguistics that estimates the time of divergence between related languages based on the proportion of shared cognates in their basic vocabularies. Developed by Morris Swadesh in 1950, the method assumes that core vocabulary items change at a relatively constant rate over time, allowing linguists to calculate a 'time depth'—how long ago two languages shared a common ancestor. Though controversial due to its restrictive assumptions, glottochronology provides rough temporal estimates when archaeological or written records are unavailable.

Swadesh, M. (1950). Salish internal relationships. International Journal of American Linguistics, 16(3), 157-167.

Swadesh, M. (1955). Towards greater accuracy in lexicostatistic dating. International Journal of American Linguistics, 21(2), 121-137.

Embleton, S. M. (1986). Statistics in Historical Linguistics. Bochum: Brockmeyer.


GloVe Embeddings

GloVe
global vectors
GloVe Kelime Gömülmeleri

GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.

Global word-word co-occurrence statistics

Output: Dense static word vectors

Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.

word2vec-embeddings tf-idf bert-embeddings collocation-analysis


GMM Estimation

Generalized Method of Moments Estimation

Genelleştirilmiş Momentler Yöntemi (GMM)

Lars Peter Hansen; Arellano & Bond (dynamic panel)

The Generalized Method of Moments is a general-purpose econometric estimator that recovers parameters from population moment conditions, introduced by Lars Peter Hansen in 1982. It is widely used for instrumental-variable estimation, dynamic panel-data models (the Arellano-Bond estimator), and time-series applications.

Generalized Method of Moments (minimises a weighted quadratic form of sample moments)

Hansen, L. P. (1982). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50(4), 1029-1054.

Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277-297.

ols-regression two-stage-least-squares panel-fixed-effects tobit-model instrumental-variables


GMRES

GMRES (Generalized Minimal Residual) is an iterative method for solving large sparse non-symmetric or nonsymmetric linear systems Ax = b, developed by Saad and Schultz in 1986. It builds an orthonormal Krylov basis using Arnoldi's method and solves a least-squares problem to minimize residual at each iteration.

Iterative linear solver for non-symmetric systems

Saad, Y., & Schultz, M. H. (1986). GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM Journal on Scientific and Statistical Computing, 7(3), 856–869.

Walker, H. F. (1988). Implementation of the GMRES method using Householder reflections. SIAM Journal on Scientific and Statistical Computing, 9(1), 152–163.

Saad, Y. (2003). Iterative Methods for Sparse Linear Systems (2nd ed.). SIAM.

conjugate-gradient-method bicg lsmr minres


Graph Neural Network

Grafik Sinir Ağı (GNN)
GNN
graph neural net
graph convolutional network

A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.

Classification, explanation & relationship learning

Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.

Veličković, P. et al. (2018). Graph Attention Networks. ICLR.

Hamilton, W.L. (2020). Graph Representation Learning. Morgan & Claypool.

cnn-image-classification xgboost random-forest svm-classification


GNSS RTK

RTK
Real-Time Kinematic positioning
GNSS-RTK
differential GNSS

Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) is a high-precision positioning technique that uses carrier phase measurements from a reference receiver at a known location to correct the position estimates of a rover receiver in real time. Developed in the 1980s, RTK exploits spatial correlation of atmospheric errors to achieve centimeter-level accuracy within tens of kilometers of the reference station. RTK is now standard in surveying, construction, autonomous vehicles, and precision agriculture.

Teunissen, P. J. G., & Kleusberg, A. (Eds.). (2003). GPS for Geodesy (2nd ed.). Springer-Verlag.

Hofmann-Wellenhof, B., Lichtenegger, H., & Wasle, E. (2005). GNSS Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more. Springer-Verlag.

Groves, P. D. (2008). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House.


GOAL-PROGRAMMING

GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Multi-objective optimisation — weighted/lexicographic goal deviation minimisation

Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Going Concern Evaluation

Going Concern Assessment Framework for Financial Statement Audits

Going Concern Analysis
Entity Viability Assessment
Continuity Evaluation

American Institute of Certified Public Accountants (AICPA) and International Auditing and Assurance Standards Board (IAASB)

Going Concern Evaluation is an auditor framework for assessing whether the entity being audited will be able to continue operating and meeting its obligations in the foreseeable future (typically, one year from the financial statement date). Required by auditing standards, this assessment examines financial and operational indicators of distress and evaluates management's plans to address concerns, ultimately determining whether financial statements require modification or special disclosure.

Audit assessment and reporting framework

American Institute of Certified Public Accountants (AICPA). (2015). Evaluating Compliance with Going Concern Assumption. AU-C Section 570. AICPA Professional Standards.

International Auditing and Assurance Standards Board (IAASB). (2015). The Auditor's Responsibilities Relating to Going Concern. ISA 570. IAASB Publications.


Golden Ratio Analysis

Fibonacci Proportion Evaluation
Divine Proportion Assessment

Golden Ratio Analysis is a method for evaluating compositional balance based on the golden ratio (phi, approximately 1.618), a mathematical proportion found throughout nature and classical art. This analysis assesses whether design elements adhere to golden ratio proportions, which some claim enhance aesthetic appeal and visual harmony.

Livio, M. (2002). The Golden Ratio: The Story of Phi, the World's Most Astonishing Number. Broadway Books.

Markowsky, G. (1992). Misconceptions about the Golden Ratio. The College Mathematics Journal, 23(1), 2–19.

Fedorov, G., Vititnev, N., & Polezhaev, A. (2014). The Myth of the Golden Ratio in Facial Attractiveness. Frontiers in Psychology, 5, 1048.

visual-balance-measurement visual-complexity-measure gestalt-principles-analysis color-harmony-analysis image-aesthetics-assessment


Goldfeld-Quandt Test

Goldfeld-Quandt Test for Heteroskedasticity

GQ Test
Goldfeld-Quandt Heteroskedasticity Test
Split-Sample Variance Ratio Test
Goldfeld-Quandt Homojenlik Testi

The Goldfeld-Quandt test, introduced by Stephen Goldfeld and Richard Quandt in 1965, is a classical diagnostic procedure for detecting heteroskedasticity in OLS regression. It operates by sorting observations according to a variable suspected of driving variance, omitting a central block, fitting separate regressions on the two tail sub-samples, and comparing their residual variances via an F-ratio. The test is particularly well-suited to situations where the error variance is believed to increase or decrease monotonically with an observed regressor.

Goldfeld, S. M., & Quandt, R. E. (1965). Some tests for homoscedasticity. Journal of the American Statistical Association, 60(310), 539–547.


Good Death Inventory

The Good Death Inventory (GDI) is a 20-item self-report measure assessing the patient's and family's perception of whether the death was 'good'—characterized by pain control, peace, meaningful closure, preparation, maintenance of dignity, and a sense that life was lived fully. Developed by Ching and colleagues in Hong Kong in 2009, the GDI operationalizes the multidimensional concept of a 'good death' into measurable dimensions, enabling clinicians and researchers to understand what makes end-of-life care meaningful and to identify deaths marked by distress or unfinished business.

Ching, J. P., Cheng, Z. H., Cheung, K. C., & Leung, K. K. (2009). Development and validation of the Good Death Inventory in Hong Kong. American Journal of Hospice and Palliative Medicine, 26(1), 56–64.

Chochinov, H. M., Hack, T., Hassard, T., Kristjanson, L. J., McClement, S., & Harlos, M. (2005). Dignity and psychotherapeutic interventions in palliative care. Journal of Palliative Care, 21(1), 23–29.

patient-dignity-inventory mcgill-quality-of-life spiritual-wellbeing-scale palliative-performance-scale comfort-care-checklist


Goodness-of-Fit

goodness of fit test
GOF test
model fit assessment

Goodness-of-fit (GOF) testing is a framework for assessing whether observed data are consistent with a hypothesized probability distribution or model. Originating from Karl Pearson's chi-square test (1900), GOF tests quantify the discrepancy between data and model predictions, yielding p-values to judge whether observed deviations are statistically significant or due to random chance.

Hypothesis testing framework for model adequacy

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157-175.

Cramér, H. (1928). On the composition of elementary errors. Skandinavisk Aktuarietidskrift, 11, 141-180.

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

r-squared mean-squared-error akaike-information-criterion bayesian-information-criterion


Gower Distance

Gower distance is a versatile metric for comparing observations with mixed variable types (continuous, ordinal, categorical, and binary). Introduced by John C. Gower in 1971, this similarity coefficient computes a general measure that ranges from 0 (completely dissimilar) to 1 (identical). It automatically scales variables to a common unit and handles missing values gracefully, making it ideal for clustering and classification on heterogeneous datasets.

General similarity coefficient for multivariate data

Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27(4), 857-874.

Gower, J. C. (1985). Properties of Euclidean and non-Euclidean distance matrices. Linear Algebra and its Applications, 67, 81-97.


GPC/SEC

Gel Permeation Chromatography Size Exclusion Chromatography

size exclusion chromatography
molecular weight determination
polymer characterization

Gel permeation chromatography (GPC), also known as size exclusion chromatography (SEC), is an analytical technique for determining the molecular weight distribution (MWD) and average molecular weight (Mw, Mn) of polymers. The method separates polymer molecules by their hydrodynamic size as they pass through a porous chromatography column: larger molecules elute first (excluded from pores), while smaller molecules are retained longer. Developed by Moore and colleagues in the 1960s, GPC/SEC is now the standard method for characterizing polymer chains, assessing polymer degradation over time, and verifying batch consistency in biomaterial production.

Striegel, A. M., Yau, W. W., Kirkland, J. J., & Bly, D. D. (2009). Modern size-exclusion liquid chromatography: practice and theory. John Wiley & Sons.

Podzimek, S. (2011). Light scattering, size exclusion chromatography and asymmetric flow field flow fractionation: promising tools for the characterization of polymers and nanoparticles. John Wiley & Sons.

Bateman, L. C., & Moore, C. G. (2017). Determination of molecular weight and molecular weight distribution. In The Chemistry and Physics of Rubber-Like Substances. Academic Press.


GPT Fine-Tuning

GPT Fine-Tuning and Instruction Adaptation

GPT İnce Ayar ve Talimat Uyarlaması
GPT fine-tuning
instruction tuning
LLM fine-tuning

GPT fine-tuning adapts pretrained autoregressive language models such as GPT-2/3/4 or LLaMA — introduced in OpenAI's 2019 work by Radford and colleagues — to domain-specific data or to instruction following via reinforcement learning from human feedback (RLHF) or DPO. It is used for instruction following, domain adaptation, and generative tasks.

Fine-tuning of pretrained autoregressive language models

Text classification, prediction & generation

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.

Ouyang, L. et al. (2022). Training Language Models to Follow Instructions with Human Feedback. NeurIPS.

lora-peft vision-transformer random-forest xgboost variational-autoencoder


GRA

GRA (Grey Relational Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Deng, J. L. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Grey relational grade (reference sequence comparison)

Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System


GRADE Evidence Profiling

Evidence certainty and recommendation strength assessment

GRADE (Grading of Recommendations Assessment, Development and Evaluation) is a systematic, transparent framework for assessing the certainty of evidence and determining the strength of clinical recommendations in healthcare. Published in 2008 by Guyatt et al., GRADE has become the international standard for guideline development, used by the World Health Organization, Cochrane, and most major clinical guideline organizations worldwide.

Research team / Guideline panel assessment

Guyatt, G., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924–926.

cochrane-risk-of-bias prisma-checklist casp-rct-checklist consort-reporting-checklist


GRM

Samejima's GRM
Derecelendirilmiş Tepki Modeli (GRM)
graded IRT model

The Graded Response Model is an item response theory model developed by Fumiko Samejima in 1969 for ordered polytomous items such as Likert-type scales. It estimates both the discriminating power of each item and a set of threshold parameters marking the boundaries between adjacent response categories, while simultaneously placing persons on a continuous latent trait scale.

Item response theory / polytomous IRT model

Item discrimination and threshold parameters; person trait (theta) estimates

Samejima, F. (1969). Estimation of Latent Ability Using a Response Pattern of Graded Scores. Psychometrika Monograph Supplement, No. 17.

Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.

rasch-model two-pl-irt three-pl-irt partial-credit-model exploratory-factor-analysis cfa lca


Gradient Boosting

Gradient Boosting Machine (Friedman's Gradient Boosting)

Gradient Boosting (GBM)
GBM
gradient boosted trees
gradient boosting machine

Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.

Ensemble (sequential boosting of decision trees)

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

xgboost lightgbm random-forest decision-tree logistic-regression


Grafting Success Evaluation

Assessment of Graft Union Formation and Physiological Compatibility

graft compatibility testing
union assessment
graft survival rate

Grafting success evaluation assesses the degree of vascular union formation and physiological compatibility between scion (upper) and rootstock (lower) in grafted plants. This method combines visual inspection of callus development, histological analysis, anatomical measurements, and physiological testing to predict long-term graft performance. It is critical for rootstock development, cultivar preservation, and commercial propagation efficiency.

Hartmann, H. T., Kester, D. E., Davies, F. T., & Geneve, R. L. (2011). Plant Propagation: Principles and Practices (8th ed.). Prentice Hall.

Ming, R., Moore, P. H., Zee, F., Fitch, M. M., Crosby, K. M., Shiba, T., & Ploetz, R. (2001). Construction of a papaya BAC library and molecular linkage mapping. Journal of the American Society for Horticultural Science, 126(6), 718–724.


Granger Causality Test

Granger test
GC test
predictive causality test
Granger non-causality test

The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.

Stationary time series (univariate or multivariate)

Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

vector-autoregression toda-yamamoto-causality-test arima-model vector-error-correction-model johansen-cointegration-test augmented-dickey-fuller-unit-root-test


Granger Causality

Granger causality test
Granger non-causality test
predictive causality test
Granger Nedensellik Testi

The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.

Restricted vs. unrestricted VAR comparison (F-test / Wald test)

Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.

var-model cointegration-test vecm-model ardl-bounds-test ols-regression


Granular Computing

Granular Computing (Information Granulation)

information granulation
computing with granules
three-way granular computing
tanecikli hesaplama

Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao

Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.

Framework for multi-granularity information processing

Process at the appropriate level of detail

Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.

Pedrycz, W., Skowron, A., & Kreinovich, V. (Eds.). (2008). Handbook of Granular Computing. Wiley.

rough-set-theory formal-concept-analysis fuzzy-cognitive-maps k-means-clustering


Graph Attention Network

Graf Dikkat Ağı (GAT)
GAT
graph attention network
attention-based graph neural network

The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).

Node classification & relational learning

Veličković, P. et al. (2018). Graph Attention Networks. ICLR.

Brody, S. et al. (2022). How Attentive are Graph Attention Networks? ICLR.

random-forest xgboost convolutional-neural-network recurrent-neural-network logistic-regression


Graph Brain Network Analysis

graph theory
brain network analysis
network neuroscience

Graph Theoretical Brain Network Analysis applies network science to understand brain organization, treating the brain as a complex network of interconnected nodes (regions) and edges (connections). Formalized by Bullmore and Sporns in 2009, graph analysis reveals fundamental organizational principles—modularity, efficiency, resilience—that characterize healthy and diseased brains.

Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069.


Graph Convolutional Network

Graph Convolutional Network (Spectral GCN for Semi-Supervised Node Classification)

GCN
graph convolutional network
spectral graph convolution
Kipf-Welling GCN
two-layer GCN

Graph Convolutional Network (GCN) is a foundational deep learning architecture for graph-structured data, introduced by Thomas N. Kipf and Max Welling at ICLR 2017. It extends the convolution operation to irregular graph domains via a first-order spectral approximation, enabling each node to aggregate feature information from its neighbors. The model became the canonical baseline for semi-supervised node classification and sparked the modern graph neural network research agenda.

Spectral graph neural network (semi-supervised node classification)

Node classification, graph representation learning

Semi-supervised (small fraction of labeled nodes)

Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France.

Hamilton, W. L. (2020). Graph Representation Learning. Morgan & Claypool (Synthesis Lectures on Artificial Intelligence and Machine Learning).

graph-attention-network graph-sage graph-isomorphism-network message-passing-neural-network convolutional-neural-network node2vec


Graph Kernels

Structured Graph Kernels
Kernel Methods on Graphs
Graf Çekirdekleri
Graph Similarity Kernels

Vishwanathan, Schraudolph, Kondor & Borgwardt

Graph kernels are positive semi-definite kernel functions that measure the similarity between two graphs by comparing their shared substructures — such as random walks, shortest paths, or subtree patterns. Introduced in a unified framework by Vishwanathan, Schraudolph, Kondor, and Borgwardt (2010), they bridge kernel methods and graph-structured data, enabling algorithms like SVMs to operate directly on graphs without requiring an explicit vectorization step.

Positive semi-definite kernel function over graphs

Polynomial in graph size for most variants

Output: Gram matrix of pairwise graph similarities

Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11, 1201–1242.


Graph Neural Network (Network Analysis)

Graph Neural Network (GCN / GAT / GraphSAGE)

GNN
GCN
GAT
GraphSAGE
Graf Sinir Ağı (GCN / GAT / GraphSAGE)

A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.

Kipf & Welling (GCN, 2017); Veličković et al. (GAT, 2018); Hamilton et al. (GraphSAGE, 2017)

Neighborhood message passing (spectral convolution / attention / inductive sampling)

Node classification, link prediction, graph classification

Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).

Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph Attention Networks. International Conference on Learning Representations (ICLR).

Hamilton, W.L. (2020). Graph Representation Learning. Morgan & Claypool.

network-embedding centrality-analysis community-detection temporal-network-analysis multilayer-network


GraphRAG

Deep Learning, Language Models, Knowledge Graphs

GraphRAG is a retrieval-augmented generation approach that augments large language models with knowledge graphs to improve answer quality and factuality. Rather than retrieving flat text passages, GraphRAG constructs and queries structured knowledge graphs extracted from documents, providing rich contextual information to the language model.

Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, M. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.

latent-diffusion-models masked-autoencoders spatial-temporal-gcn segment-anything-model


Gratitude Questionnaire

The Gratitude Questionnaire-Six (GQ-6), developed by McCullough, Emmons, and Tsang in 2002, is a 6-item measure of dispositional gratitude—the tendency to recognize and appreciate the good in one's life. Operationalizing gratitude as a stable personality trait (not just a momentary feeling), the GQ-6 assesses the capacity to notice, appreciate, and be thankful for life's blessings. Research shows that dispositional gratitude predicts well-being, life satisfaction, relationship quality, and resilience independent of personality and optimism.

McCullough, M. E., Emmons, R. A., & Tsang, J. A. (2002). The grateful disposition: A conceptual and empirical topography. Journal of Personality and Social Psychology, 82(1), 112–127.

flourishing-scale perma-scale subjective-wellbeing-scale via-character-strengths


Gravitational Microlensing

Gravitational Microlensing for Exoplanet and Dark Matter Detection

Microlensing
Gravitational Lensing Method

Gravitational microlensing is an observational technique that exploits Einstein's prediction that massive objects bend light. When a star or planet passes in front of a distant star from our perspective, its gravity acts as a lens, magnifying and distorting the background star's light. First proposed by Bohdan Paczynski in 1986, this method has discovered hundreds of exoplanets and provides unique sensitivity to low-mass planets and dark matter.

Paczynski, B. (1986). Gravitational microlensing by the galactic halo. Astrophysical Journal, 304, 1-5.

Bond, I. A., et al. (1991). Microlensing of distant blue stars. Astrophysical Journal, 378, L81-L84.

Gaudi, B. S. (2012). Microlensing surveys for exoplanets. Annual Review of Astronomy and Astrophysics, 50, 411-453.


Gravitational Wave Matched Filtering

Gravitational Wave Signal Detection via Matched Filtering

template-based detection
correlation filtering
GW signal extraction

Matched filtering is a signal processing technique used to detect gravitational waves by correlating detector data with theoretical waveform templates. When two massive objects (black holes, neutron stars) merge, they emit gravitational waves that pass through Earth, producing tiny distortions in laser interferometers like LIGO and Virgo. Matched filtering, formalized by Harry Nyquist, optimally extracts these signals from noise, enabling the detection of mergers billions of light-years away.

Abbott, B. P., et al. (2016). Observation of Gravitational Waves from a Binary Black Hole Merger. Physical Review Letters, 116(6), 061102.

Weedman, D. (2010). Statistics of Matched Filtering and Bayes Theorem Applied to Gravitational Wave Data Analysis. Classical and Quantum Gravity, 10(9), S211.

Allen, B., Anderson, W. G., Brady, P. R., Brown, D. A., & Creighton, J. D. (2012). FINDCHIRP: An Algorithm for Detection of Gravitational Waves from Inspiraling Compact Binaries. Physical Review D, 85(12), 122006.


Gravity Assist

A gravity assist (or swing-by) maneuver uses the gravitational field of a planet or other celestial body to alter a spacecraft's trajectory and velocity without expending fuel. Discovered by Michael Minovitch at JPL in 1961, this technique is crucial for reaching distant planets economically. It works by exploiting the relative motion between the spacecraft, the assisting body, and the Sun.

Minovitch, M. A. (1961). The determination and characteristics of ballistic interplanetary trajectories under the influence of multiple planetary gravitational fields. Technical Report 32-464, Jet Propulsion Laboratory.

Laplace, P. S. (1799). Traité de Mécanique Céleste. Bachelier.

Curtis, H. D. (2013). Orbital Mechanics for Engineering Students (3rd ed.). Butterworth-Heinemann.

hohmann-transfer n-body-simulation orbit-determination light-curve-analysis


Greeks via Automatic Differentiation

Automatic Differentiation for Greeks Computation

AD Greeks
Algorithmic Differentiation
Autodiff

Automatic differentiation (AD) is a computational technique for computing derivatives (Greeks) by differentiating the computer code that computes the option price. AD avoids manual derivation of formulas and finite-difference approximations, yielding exact sensitivities with machine precision. It has become essential for real-time risk management in modern trading systems.

Giles, M. B. (2008). Adjoint code by automatic differentiation. Journal of Computational Finance, 12(1), 1-18.

Homescu, C. (2011). Adjoints and automatic differentiation in computational finance. arXiv:1107.1188.


Green Building Rating System

Green Building Rating System Assessment and Certification

LEED certification
green building assessment
sustainability rating

Sustainability assessment and environmental rating

Green Building Rating Systems are standardized frameworks for assessing and certifying the environmental performance and sustainability of buildings. The most widely known is LEED (Leadership in Energy and Environmental Design), established by the U.S. Green Building Council in 1998. Similar systems exist globally (BREEAM in UK, Passivhaus in Europe), each using structured criteria to evaluate design and performance across multiple environmental dimensions.

multi-criteria sustainability rating system

U.S. Green Building Council (2021). LEED v4.1 for Building Design and Construction. USGBC.

BRE Global (2018). BREEAM New Construction Technical Manual. BRE Group.

Cole, R. J. (2005). Building Environmental Assessment Methods: Redefining Intentions and Roles. Building Research and Information, 33(6), 455-467.


Green Infrastructure Design

Planning and Implementation of Nature-Based Urban Systems

GI design
natural infrastructure
nature-based solutions
ecosystem-based adaptation

Green infrastructure (GI) design is the planning and implementation of natural or nature-based systems (vegetation, soils, water bodies) integrated into urban environments to provide multiple ecosystem services: stormwater management, air quality improvement, heat island mitigation, biodiversity habitat, recreation, and social well-being. Emerged in the 2000s as a sustainability paradigm, green infrastructure combines landscape design, hydrology, ecology, and urban planning to create multifunctional spaces that serve practical and aesthetic goals.

Freeman, R. C. (2005). Green Infrastructure: Intelligent Landscapes for the Twenty-First Century. Routledge.

U.S. Green Building Council. (2012). LEED Reference Guide for Building Design and Construction. USGBC.

Tzoulas, K., Korpela, K., Vigo, S., et al. (2007). Promoting Ecosystem and Human Health in Urban Areas Using Green Infrastructure: A Literature Review. Landscape and Urban Planning, 81(3–4), 167–178.


GPIS

sustainable consumption intentions and willingness to pay

William B. Dodds, Kent Monroe, Dhruv Grewal

The Green Purchase Intention Scale (GPIS) measures consumers' stated willingness and likelihood of purchasing environmentally friendly products, including their intention to pay premium prices for eco-labeled goods and their perceived value of sustainable alternatives. Developed from consumer behavior and willingness-to-pay frameworks (Dodds, Monroe, & Grewal, 1991; expanded by Thøgersen and others), the GPIS bridges environmental attitudes and actual purchasing behavior, a critical gap in sustainability research. The scale is widely used in marketing research, environmental policy evaluation, and studies examining whether environmental concern translates into purchasing decisions.

Self-report intention and willingness-to-pay scale

Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of Marketing Research, 28(3), 307–319.

Kim, Y., & Choi, S. M. (2005). Antecedents of green purchase intention: An examination of collectivism, environmental concern, and PCE. Advances in Consumer Research, 32, 592–599.

Thøgersen, J., & Crompton, T. (2009). Simple and painless? The limitations of spillover in environmental campaigning. Journal of Consumer Policy, 32(2), 141–163.


Greenhouse Climate Control

Automated Environmental Regulation for Optimized Plant Growth and Development

climate management
environmental control
HVAC optimization

Greenhouse climate control integrates measurement, modeling, and automated actuation to maintain optimal temperature, humidity, light, and CO₂ concentrations for plant growth. Modern systems use sensors and control algorithms to respond dynamically to external weather and internal plant needs. This approach increases yield, shortens crop cycles, reduces disease pressure, and improves energy efficiency compared to manual or static setpoint controls.

Stanghellini, C. (2003). Transpiration in greenhouse horticulture: An introduction. Acta Horticulturae, 618, 101–111.

Castilla, N. (2005). Greenhouse Technology and Management (2nd ed.). CAB International.


Gregory-Hansen Test

Gregory-Hansen Cointegration Test with Regime Shift

Residual-Based Cointegration Test with Structural Break

The Gregory-Hansen test, introduced by Allan Gregory and Bruce Hansen in 1996, extends the standard Engle-Granger cointegration framework to allow for a single unknown structural break in the cointegrating relationship. It is designed for researchers who suspect that the long-run equilibrium between integrated variables may have shifted at some point during the sample period, and who wish to test for cointegration without presupposing the break date.

Residual-based structural break cointegration test

No cointegration (even allowing for a regime shift)

Level shift (C), Level shift with trend (C/T), Regime shift (C/S)

Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99–126.


GREY-ARAS

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

Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])

Zavadskas & Turskis (2010). Grey Additive Ratio Assessment. Informatica

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Grey Clustering

Grey Clustering (Grey Incidence / Whitenization)

Grey Whitenization Weight Function Clustering

Grey Clustering is a classification method from grey systems theory that assigns objects to predefined grey classes using whitenization weight functions. Developed within the framework of Deng Julong's grey system theory and systematized by Sifeng Liu, it is particularly suited for situations involving small sample sizes, incomplete information, or uncertain data—conditions common in engineering assessments, environmental monitoring, and socioeconomic evaluation. The method quantifies how strongly each object belongs to each grey class and makes a crisp assignment based on maximum clustering coefficients.

Input: Small, uncertain, or incomplete datasets

Output: Cluster membership via whitenization weight functions

Liu, S., & Lin, Y. (2010). Grey Systems: Theory and Applications. Springer.


GREY-CODAS

GREY-CODAS (Grey-CODAS — Grey extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research


GREY-COPRAS

GREY-COPRAS (Grey-COPRAS — Grey extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Kaklauskas, A. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Zavadskas, E. K., Kaklauskas, A., Turskis, Z., Tamosaitiene, J. (2009). Multi-Attribute Decision-Making Model by Applying Grey Numbers. Informatica


GREY-EDAS

GREY-EDAS (Grey-EDAS — Grey extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., Turskis, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., Turskis, Z. (2017). An Extension of the EDAS Method Based on the Use of Interval Grey Numbers. Studies in Informatics and Control


GREY-GRA

GREY-GRA (Grey-GRA — Grey extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Deng, J. L. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System


Grey Literature Search

Systematic Search and Retrieval of Grey Literature

grey literature
gray literature
unpublished literature

1990s (formalized in systematic review guidelines)

Information specialists and systematic review methodologists (Cochrane Collaboration, Health Technology Assessment)

Grey literature comprises documents and data not published through conventional commercial channels—including theses, government reports, clinical trial registries, conference abstracts, organizational policy documents, and working papers. Unlike journal articles, grey literature is not indexed in MEDLINE or Scopus and often lacks peer review. However, it is crucial for systematic reviews because it may contain null or negative findings that are less likely to be published (publication bias). Systematic grey literature searching is now a standard component of evidence synthesis and is recommended by the Cochrane Collaboration, PRISMA, and other methodological guidelines.

Rothstein, H. R., & Hopewell, S. (2009). Grey literature. In J. P. Higgins & S. Green (Eds.), Cochrane handbook for systematic reviews of interventions (Version 5.0.2, Chapter 13). The Cochrane Collaboration.

Wood, A. M., Egger, M., Gluud, L. L., Schulz, K. F., Jüni, P., Altman, D. G., & Gluud, C. (2008). Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ, 336(7644), 601–605.

Mahood, Q., Van Eerd, D., & Irvin, E. (2014). Searching for grey literature for systematic reviews: challenges and benefits. Research Synthesis Methods, 5(4), 353–364.

pico-framework boolean-search-operators systematic-search-strategy citation-analysis


GREY-MABAC

GREY-MABAC (Grey-MABAC — Grey extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamucar, D., Cirovic, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pamucar, D., Cirovic, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications


GREY-MARCOS

GREY-MARCOS (Grey-MARCOS — Grey extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering


GM(1,1) Grey Forecasting

GM(1,1)
grey prediction model
grey forecasting
gri tahmin modeli

GM(1,1) is the core forecasting model of grey system theory, introduced by Julong Deng in 1982, designed to predict from very few observations and incomplete information — situations where classical time-series models like ARIMA need far more data. It accumulates the raw series to expose a hidden exponential trend, fits a first-order grey differential equation, and projects future values, making it popular in engineering, energy, and management forecasting with short data records.

Few observations, incomplete information

Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.

grey-relational-analysis arima exponential-smoothing case-based-reasoning


GREY-MOORA

GREY-MOORA (Grey-MOORA — Grey extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkic, D., Magdalinovic, N., Jovanovic, R., Stojanovic, S. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Stanujkic, D., Magdalinovic, N., Jovanovic, R., Stojanovic, S. (2012). An objective multi-criteria approach to optimization using MOORA method and interval grey numbers. Technological and Economic Development of Economy


GREY-PROJECTION

GREY-PROJECTION (Grey-Projection — Grey extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Liu, S., Lin, Y. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Liu, S., Lin, Y. (2013). Grey systems-based projection for MCDM. Grey Systems: Theory and Application


GREY-PROMETHEE

GREY-PROMETHEE (Grey-PROMETHEE — Grey extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Li, P., Wei, C. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Li, P., Wei, C. (2011). Grey PROMETHEE for multi-criteria decision making. Expert Systems with Applications


GREY-SAW

GREY-SAW (Grey-SAW — Grey extension of SAW) is a ranking 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


GREY-TODIM

GREY-TODIM (Grey-TODIM — Grey extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Gomes, L. F. A. M., Lima, M. M. P. P. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

(). UNCONFIRMED — GREY-TODIM specific seminal not confirmed via systematic literature search. PENDING


GREY-TOPSIS

GREY-TOPSIS (Grey-TOPSIS — Grey extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Bagocius, V. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Zavadskas, E. K., Turskis, Z., Bagocius, V. (2015). Multi-criteria selection of a deep-water port in the Eastern Baltic Sea. Applied Soft Computing


GREY-VIKOR

GREY-VIKOR (Grey-VIKOR — Grey extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Chang, C. L., Liu, P. H., Wei, C. C. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Chang, C. L., Liu, P. H., Wei, C. C. (2001). Failure mode and effects analysis using grey theory. Integrated Manufacturing Systems


GREY-WASPAS

GREY-WASPAS (Grey-WASPAS — Grey extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of Weighted Aggregated Sum Product Assessment. Electronics and Electrical Engineering


Grey Wolf Optimizer

GWO
Gri Kurt Optimizasyonu
Gri Kurt Optimizasyonu (GWO)

Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis

The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.

Social hierarchy and cooperative hunting behaviour of grey wolves (Canis lupus)

Alpha (α), Beta (β), Delta (δ), Omega (ω)

a — linearly decreases from 2 to 0 over iterations, balancing exploration and exploitation

Continuous, unconstrained and constrained optimisation

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.

Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S. (2018). Grey Wolf Optimizer: A Review of Recent Variants and Applications. Neural Computing and Applications, 30(2), 413-435.

whale-optimization-algorithm particle-swarm-optimization genetic-algorithm simulated-annealing tabu-search bayesian-optimization


GEQ

The Grief Experience Questionnaire (GEQ) is a multidimensional measure developed by Barrett and Schneweis in 1980 to assess the breadth of emotional, cognitive, and existential experiences reported by bereaved individuals. Rather than focusing on pathology or symptom severity, the GEQ captures the diverse phenomenology of grief—including yearning, social withdrawal, guilt, anger, disorientation, and existential questioning—providing a comprehensive portrait of the grief experience.

Barrett, R. K., & Schneweis, K. C. (1980–1981). An empirical search for stages of grief. Omega, 11(2), 97–110.

texas-revised-inventory-grief inventory-complicated-grief hogan-grief-reaction-checklist anticipatory-grief-scale


Griffith Fracture Mechanics

Griffith's Theory of Brittle Fracture and Crack Growth

Brittle fracture theory
Energy release rate
Linear elastic fracture mechanics

Griffith's theory of brittle fracture explains how small flaws or cracks in materials grow unstably, leading to sudden catastrophic failure. Formulated by Alan A. Griffith in 1921 through experiments on glass fibers, this theory balances the elastic energy released by crack growth against the surface energy required to create new material surfaces. It predicts that materials fail at stresses far below their theoretical strength due to the stress concentration around pre-existing flaws.

Theoretical model for brittle fracture and crack propagation

Griffith, A. A. (1921). The phenomena of rupture and flow in solids. Philosophical Transactions of the Royal Society A, 221, 163-198.

Irwin, G. R. (1957). Analysis of stresses and strains near the end of a crack traversing a plate. Journal of Applied Mechanics, 24(3), 361-364.

Anderson, T. L. (2017). Fracture Mechanics: Fundamentals and Applications (4th ed.). CRC Press.

modal-analysis design-for-manufacturing-and-assembly tolerance-stack-up elastohydrodynamic-lubrication


Grit Scale

GRIT
Perseverance and Passion Scale
Duckworth Grit Scale

The Grit Scale is a 12-item measure assessing grit—the combination of perseverance (sustained effort despite obstacles) and passion (consistent interest and commitment) for long-term goals. Developed by Angela Duckworth and colleagues in 2007, the GRIT operationalizes grit as a distinct personality construct predicting achievement in challenging domains. The measure has become widely used in educational and organizational research examining how motivation and persistence relate to success and well-being.

Perseverance and long-term goal commitment measure

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101.

Duckworth, A. L. (2016). Grit: The power of passion and perseverance. Scribner.

Credé, M., Tynan, M. C., & Lowe, G. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492–511.


Ground-Penetrating Radar

Ground-Penetrating Radar (GPR) is a near-surface geophysical method that uses high-frequency electromagnetic pulses (typically 10 MHz to 2.5 GHz) to image shallow subsurface structures with exceptional spatial resolution. Pioneered by Davis and Annan in 1989, GPR is widely used in archaeology, civil engineering, environmental assessment, and shallow mineral exploration due to its ability to resolve features at decimeter to centimeter scales.

Shallow subsurface electromagnetic pulse detection

Davis, J. L., & Annan, A. P. (1989). Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy. Geophysical Prospecting, 37(5), 531-551.

Jol, H. M. (2009). Ground penetrating radar: Theory and applications. Elsevier.


Grounded Theory

Grounded Theory (GT) is a systematic qualitative research methodology in which theory emerges directly from data through iterative analysis, rather than being imposed before data collection. Developed by Barney Glaser and Anselm Strauss in 1967, GT prioritizes generating explanatory frameworks grounded in evidence.

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.

Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory (2nd ed.). Sage Publications.

Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Sage Publications.

phenomenological-research thematic-analysis constant-comparison-analysis theoretical-saturation narrative-inquiry


Groundwater Contamination Modeling

Simulation of Contaminant Transport in Groundwater

groundwater transport
contaminant plume modeling
subsurface flow and transport
GWHC modeling

Groundwater contamination modeling is a quantitative approach to predict the migration of dissolved and suspended contaminants (chemical spills, landfill leachate, petroleum, radionuclides) through subsurface aquifers and toward receptors (drinking water wells, surface water bodies, ecosystems). Developed systematically in the 1980s–1990s by the USGS and hydrogeologists, these models couple flow equations (Darcy's law) with advection-dispersion transport and geochemical reactions to forecast contaminant arrival times and plume extent.

Fetter, C. W., Boving, T. B., & Kreamer, D. K. (2018). Contaminant Hydrogeology (3rd ed.). Waveland Press.

US Geological Survey. (2003). MODFLOW-2000, the U.S. Geological Survey Modular Finite-Difference Ground-Water Flow Model. USGS Open-File Report 03-123.

Domenico, P. A., & Schwartz, F. W. (1998). Physical and Chemical Hydrogeology (2nd ed.). John Wiley & Sons.


Grover's Algorithm

Grover's Algorithm is a quantum algorithm for searching an unsorted database, offering a quadratic speedup over classical linear search. Proposed by Lov Grover in 1996, it exploits quantum superposition and amplitude amplification to find a target item among N items in O(√N) queries, compared to the classical O(N) requirement.

Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC), 212–219.

Grover, L. K. (1997). Quantum mechanics helps in searching for a needle in a haystack. Physical Review Letters, 79, 325–328.

Brassard, G., Hoyer, P., Tapp, A. (2002). Quantum amplitude amplification and estimation. arXiv preprint quant-ph/0005055.


Growth Curve Fitting in Livestock

Growth Curve Fitting and Trajectory Analysis in Livestock

growth model fitting
trajectory analysis
growth kinetics modeling

Animal Biologists and Agricultural Statisticians

Growth curve fitting is the mathematical modeling of animal body weight and size changes over time. Developed by animal biologists and statisticians in the 1970s-1980s (Fitzhugh), the method applies nonlinear regression to weight data, extracting parameters that characterize growth rate, time to maturity, and asymptotic mature weight. Curve fitting supports comparisons of genetics, nutrition, and management effects on growth efficiency and enables prediction of market weight and slaughter timing.

Menchaca, M. A., & Chase, C. C. (2002). Body measurements and condition scores for beef cattle. Veterinary Clinics of North America: Food Animal Practice, 19(3), 387-405.

Brown, J. L., Cummins, L., Herring, W., Waldner, T., & Roesler, R. (2003). Comparative evaluation of growth models for modeling beef cattle growth. Journal of Animal Science, 81(7), 1813-1820.

Fitzhugh, H. A. (1976). Analysis of growth curves and strategies for altering their shape. Journal of Animal Science, 42(4), 1036-1051.


AGHDA

Adult Growth Hormone Deficiency Assessment Scale

Pituitary hormone deficiency quality of life

Anthony Hunt, Garry Werther, Peter Wrightson

The AGHDA is a 25-item disease-specific quality of life questionnaire designed to assess the burden of adult growth hormone (GH) deficiency. Developed by Hunt, Werther, and colleagues in 2000, it evaluates symptoms and functional impairments directly related to GH deficiency, including fatigue, reduced muscle strength, weight gain, and psychological difficulties. The instrument is widely used in endocrinology practice and clinical trials to quantify the impact of GH replacement therapy.

Hunt, A. E., Werther, G. A., & Wrightson, P. (2000). The utility of AGHDA in identifying GH-deficient adults. Clin Endocrinol (Oxf), 52(3), 341-346.

Abs, R., Feldt-Rasmussen, U., Mattsson, A. F., et al. (1999). Assessment of GH status in adults with childhood-onset GH deficiency. Clin Endocrinol (Oxf), 50(4), 457-463.


GMM

Büyüme Karışım Modeli (Growth Mixture Model — GMM)

The Growth Mixture Model, introduced by Muthén and Shedden in 1999, is a longitudinal latent variable method that identifies distinct subpopulations — latent trajectory classes — each following its own growth curve over time. It extends the standard Latent Growth Curve (LGC) model by allowing the sample to be composed of an unknown mixture of classes with different intercepts, slopes, and variance structures.

Latent class / longitudinal growth model

Latent trajectory classes with class-specific growth parameters

Continuous repeated measures (longitudinal / panel)

Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469.

lca hlm sem exploratory-factor-analysis multiple-imputation latent-class-growth-analysis


GRU

Kapılı Tekrarlayan Birim (GRU)
gated recurrent unit
gated recurrent network

The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.

Prediction, forecasting & classification on sequences

Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.

Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. NIPS 2014 Deep Learning Workshop. arXiv:1412.3555

bidirectional-rnn seq2seq attention-mechanism random-forest xgboost


Guttman Scale

Louis Guttman's Cumulative Unidimensional Scaling Method

Cumulative scale
Scalogram analysis
Guttman scaling
Unidimensional cumulative scale

Guttman scaling is a methodology for constructing unidimensional scales with a cumulative property, developed by Louis Guttman in 1944. The method assumes that items form a perfect or near-perfect hierarchy: if a respondent endorses a harder item, they must endorse all easier items below it. This creates a reproducible scale structure useful for measuring constructs with ordinal properties such as difficulty, intensity, or severity.

Cumulative unidimensional scaling methodology

Guttman, L. (1944). A basis for scaling qualitative data. American Sociological Review, 9(2), 139-150.

Guttman, L. (1950). The basis for scalogram analysis. In S. A. Stouffer et al. (Eds.), Measurement and Prediction. Princeton, NJ: Princeton University Press.

Menzel, H. (1953). A new coefficient for scalogram analysis. Public Opinion Quarterly, 17(2), 268-280.

likert-scale-construction factor-analysis-scale content-validity-ratio floor-ceiling-effect


Genome-wide association study in educational research

Genome-Wide Association Study Applied to Educational Outcomes

GWAS in education
educational GWAS
GWAS for cognitive traits
genomic study of educational attainment

2013 (first large-scale educational attainment GWAS); refined through major studies in 2016, 2018, 2022

Social Science Genetic Association Consortium (SSGAC); Rietveld et al. 2013 pioneered educational attainment GWAS

A genome-wide association study (GWAS) applied to educational research scans millions of single-nucleotide polymorphisms (SNPs) across the human genome to identify genetic variants statistically associated with educational outcomes such as years of schooling, degree attainment, or cognitive test scores. Large consortia — most prominently the Social Science Genetic Association Consortium — have conducted landmark studies in hundreds of thousands to millions of individuals, establishing GWAS as the principal genomic tool for understanding the heritable architecture of educational phenotypes.

Quantitative genomic association analysis

Genome-wide SNP array data (millions of variants) linked to educational outcome phenotypes (years of schooling, degree attainment, cognitive test scores)

Okbay, A., Turley, P., Georgios, K., et al. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437–449.

Lee, J. J., Wedow, R., Okbay, A., et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121.

polygenic-score-analysis linkage-disequilibrium mendelian-randomization principal-component-analysis meta-analysis heritability-estimation


H-Index

Jorge Hirsch, University of California San Diego

The h-index, or Hirsch index, is a quantitative metric proposed by physicist Jorge Hirsch in 2005 to measure researcher productivity and citation impact simultaneously. A researcher has an h-index of h if they have published at least h papers, each cited at least h times. For example, an h-index of 20 means the researcher has 20 papers each cited at least 20 times. The h-index is widely used in research evaluation, hiring, and promotion decisions, though experts debate its limitations. It provides a single number balancing quantity of publications against quality of citations, offering an intuitive summary of research career impact.

Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences USA, 102(46), 16569-16572.

Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152.

Bornmann, L., Mutz, R., & Daniel, H. D. (2010). The h index for Journal Impact Factor: A new approach for assessing journal impact. Journal of Informetrics, 4(3), 358-365.

web-of-science scopus-database impact-factor journal-citation-reports pubmed-medline


H-infinity Control

H∞ Control
Robust Control
Minimax Control

H-infinity (H∞) control is a robust control method that minimizes the worst-case gain from disturbances to controlled outputs, formulated as a minimax optimization problem. Pioneered by Zames in the early 1980s, H∞ control provides a principled way to design feedback controllers that tolerate model uncertainty, unmodeled dynamics, and disturbances while maintaining stability and performance, making it essential for applications requiring guaranteed robustness.

Zames, G. (1981). Feedback and optimal sensitivity: Model reference transformations, multiplicative seminorms, and approximate inverses. IEEE Transactions on Automatic Control, 26(2), 301-320.

Francis, B. A. (1987). A Course in H∞ Control Theory. Lecture Notes in Control and Information Sciences, Springer-Verlag.

Zhou, K., Doyle, J. C., & Glover, K. (1996). Robust and Optimal Control. Prentice Hall.

linear-quadratic-regulator model-predictive-control adaptive-control feedback-linearization


HACCP

HACCP (Hazard Analysis and Critical Control Points) is a systematic preventive approach to food safety developed in the late 1980s by Bryan and colleagues. It identifies potential biological, chemical, and physical hazards in food production processes and establishes critical control points to prevent contamination. HACCP is now globally recognized as the gold standard for food safety management.

Bryan, F. L. (1992). Hazard Analysis Critical Control Point Evaluations: A Guide to Identifying Hazards and Assessing Risks Associated with Food Preparation and Storage. Journal of Food Protection, 55(1), 51-59.

Codex Alimentarius Commission (1997). HACCP System and Guidelines for Its Application.


Hospital Anxiety and Depression Scale

The Hospital Anxiety and Depression Scale (HADS) is a 14-item self-report instrument measuring anxiety and depression symptoms in medically ill populations. Developed by Zigmond and Snaith in 1983, the HADS was specifically designed for hospital and general medical settings where somatic symptoms of medical illness may confound assessment. It remains the standard anxiety-depression measure in medical, oncology, and cardiac populations worldwide.

Anxiety and depression screening in medical populations

Zigmond, A. S., & Snaith, R. P. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica, 67(6), 361-370.

Bjelland, I., Dahl, A. A., Haug, T. T., & Neckelmann, D. (2002). The validity of the Hospital Anxiety and Depression Scale: An updated literature review. Journal of Psychosomatic Research, 52(2), 69-77.


ALPPQ

Hair Loss Impact Questionnaire
Alopecia Areata QoL

The Hair Loss Impact Questionnaire (Alopecia Areata Patient Priority Outcomes Questionnaire, ALPPQ) is a disease-specific, patient-administered quality-of-life measure assessing the psychosocial and functional burden of alopecia areata, a chronic autoimmune disorder causing patchy hair loss. Alopecia areata affects appearance, self-esteem, and social functioning disproportionately, often causing depression and anxiety. The ALPPQ captures these impacts, ensuring that treatment efficacy encompasses meaningful quality-of-life outcomes. It is increasingly used in clinical trials and observational studies of alopecia areata therapeutics.

Gupta AK, Talukder M. Alopecia areata: autoimmune basis of hair loss and available treatment options. Can J Dermatol. 2014;12(5):289-304.

Strober BE, Mengesha YM, Clay FJ, et al. Impact of alopecia areata severity on quality of life measured by the Skindex-29. J Am Acad Dermatol. 2005;52(3):S45.

poem skindex-29 dermatology-life-quality-index-children acne-qol


Hallucination Detection

Hallucination Detection (Factual Consistency)

Hallüsinasyon Tespiti (Factual Consistency)

2020 (faithfulness framing); 2023 (SelfCheckGPT)

Established as a formal task by Maynez et al. (2020); SelfCheckGPT zero-resource variant by Manakul et al. (2023)

Hallucination detection is a natural-language-processing pipeline that measures whether the output of a language model is consistent with a reference source document or with verifiable facts. Formalised as a faithfulness evaluation task by Maynez et al. (2020) and extended to a zero-resource black-box setting by Manakul et al. (2023) with SelfCheckGPT, the approach is used to flag unreliable LLM outputs in high-stakes domains such as medicine, law, and journalism.

NLP evaluation / quality-assurance pipeline

NLI-based / QA-based / sampling-based (SelfCheckGPT)

Output: Consistency / faithfulness score or binary hallucination label per claim or sentence

Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906-1919.

Manakul, P., Liusie, A., & Gales, M.J.F. (2023). SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 9004-9017.

sentiment-analysis text-classification bert-embeddings named-entity-recognition question-answering


Halo Occupation Distribution

Halo Occupation Distribution Modeling for Galaxy Clustering

Halo Occupation Distribution (HOD) modeling is a framework for relating observed galaxy clustering to the distribution of galaxies within dark matter halos. Developed by Jia Peacock and others around 2000, HOD provides a flexible, physically motivated approach to interpreting galaxy surveys and understanding how galaxies populate dark matter halos across cosmic time.

Peacock, J. A., & Smith, R. E. (2000). Halo occupation numbers and galaxy bias. Monthly Notices of the Royal Astronomical Society, 318(4), 1144-1156.

Berlind, A. A., & Weinberg, D. H. (2002). The statistics of galaxy clustering: are models with constant bias sufficient? Astrophysical Journal, 575(2), 587-605.

Zheng, Z., et al. (2007). Measuring galaxy clustering: imprints of galaxy formation on large scales. Astrophysical Journal, 667(2), 760-779.


Halstead Complexity

Halstead metrics
program length
volume metric

Halstead Complexity Metrics are a set of static code analysis measures developed by Maurice Halstead in 1977 that quantify software quality using operator and operand counts. Metrics like program volume, difficulty, and effort estimate code complexity, maintainability, and defect likelihood from source code structure alone.

Halstead, M. H. (1977). Elements of Software Science. Elsevier.

Kitchenham, B. A., Pickard, L. M., & Linkman, S. J. (1995). An empirical study of source code defects. IEEE Transactions on Software Engineering, 21(2), 147–156.

Harrison, W. (2007). Using metrics to evaluate software system maintainability. IEEE Software, 24(4), 44–50.

cyclomatic-complexity maintainability-index code-coverage defect-prediction


Hamilton Anxiety Rating Scale

The Hamilton Anxiety Rating Scale (HAM-A) is a clinician-administered assessment tool for quantifying the severity of anxiety symptoms in adults. Developed by Max Hamilton in 1959, it remains one of the most widely used instruments for evaluating anxiety in clinical and research settings. The scale measures both psychological and somatic manifestations of anxiety across 14 items.

Hamilton, M. (1959). The assessment of anxiety states by rating. British Journal of Medical Psychology, 32(1), 50-55.

Maier, W., Buller, R., Philipp, M., & Heuser, I. (1988). The Hamilton Anxiety Rating Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders. Journal of Affective Disorders, 14(1), 61-68.

panas hads zung-anxiety-scale dass-21 gds-geriatric-depression