Statistics — Page 18
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Frees Test
The Frees test, introduced by Edward Frees in 1995, is a non-parametric diagnostic procedure for detecting cross-sectional dependence in panel data. It is designed for settings where N (number of units) is large and T (time periods) is mode...
Frequency analysis
Frequency analysis is a fundamental descriptive technique that tallies how often each distinct value or category appears in a dataset. It produces absolute counts, relative percentages, and cumulative frequencies, giving an immediate pictur...
Friedman test
The Friedman test is a nonparametric hypothesis test that compares three or more related conditions measured on the same blocks or subjects, serving as the rank-based alternative to repeated-measures ANOVA. It was introduced by Milton Fried...
Fuzzy ANOVA
Fuzzy ANOVA extends classical analysis of variance to fuzzy data where observations and group memberships are imprecise or uncertain. Developed by Viertl and others, Fuzzy ANOVA tests whether fuzzy-valued groups differ significantly while a...
GAMLSS
GAMLSS is a broad class of semi-parametric regression models introduced by Robert Rigby and Mikis Stasinopoulos in 2005. Unlike classical regression, which models only the mean of a response, GAMLSS allows each parameter of a chosen paramet...
GARCH
GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks...
GARCH Model
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effec...
GARCH-MIDAS
GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by...
GJR-GARCH
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 r...
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...
GMM Estimation
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, dynami...
Games-Howell Test
The Games-Howell test is a parametric post-hoc multiple comparison procedure that identifies which pairs of group means differ significantly after an omnibus ANOVA reveals a significant overall effect. Proposed by Games and Howell in 1976,...
Gamma Regression
Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear...
Gamma-Gamma Spend Model
The Gamma-Gamma model of monetary value is the standard companion to buy-till-you-die transaction models, estimating how much a customer spends per transaction so that purchase-count forecasts can be turned into monetary customer lifetime v...
Geary's C
Geary's C is a global measure of spatial autocorrelation — whether nearby locations tend to have similar values — introduced by Roy Geary in 1954. Unlike Moran's I, which is built on the covariation of values around the mean, Geary's C is b...
Geary's C
Geary's C is a global spatial autocorrelation statistic that measures whether nearby areal units share similar attribute values. Unlike Moran's I, it focuses on squared differences between adjacent pairs rather than cross-products of deviat...
Gender Gap Decomposition
Gender gap decomposition, most often implemented as the Oaxaca-Blinder decomposition, splits the mean difference in an outcome such as wages between men and women into a part explained by differences in measured characteristics (education,...
Generalized Least Squares
Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig...
Generalized Linear Model
The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects...
Geographic Regression Discontinuity
Geographic Regression Discontinuity (GRD) is a quasi-experimental design that exploits sharp geographic boundaries—borders, policy boundaries, or natural features—to estimate causal effects. Introduced by Dell (2010) and others, it compares...
Geographically Weighted Regression
Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set...
Getis-Ord Gi*
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...
Giacomini-White Test
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. U...
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...