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Modelos Aditivos Generalizados para Localização, Escala e Forma (GAMLSS)×Regressão Quantílica×
ÁreaEstatísticaEconometria
FamíliaRegression modelRegression model
Ano de origem20051978
Autor originalRobert Rigby & Mikis StasinopoulosKoenker & Bassett
TipoSemi-parametric distributional regression modelConditional quantile regression
Fonte seminalRigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C, 54(3), 507–554. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Outros nomesDistributional Regression, Flexible Regression and Smoothing, GAMLSS Framework, Konum, Ölçek ve Şekil için Genelleştirilmiş Toplamlı Modellerconditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados25
ResumoGAMLSS is a broad class of semi-parametric regression models introduced by Robert Rigby and Mikis Stasinopoulos in 2005. Unlike classical regression, which models only the mean of a response, GAMLSS allows each parameter of a chosen parametric distribution — location (e.g., mean), scale (e.g., variance), and shape (e.g., skewness, kurtosis) — to be modeled as an additive function of covariates. This makes it possible to capture heteroscedasticity, skewness, and heavy tails simultaneously within a single unified framework.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
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ScholarGateComparar métodos: GAMLSS · Quantile Regression. Recuperado em 2026-06-17 de https://scholargate.app/pt/compare