Compară metode

	Modele Aditive Generalizate pentru Locație, Scară și Formă (GAMLSS)	Regresia cuantilică
Domeniu	Statistică	Econometrie
Familie	Regression model	Regression model
Anul apariției	2005	1978
Autorul original	Robert Rigby & Mikis Stasinopoulos	Koenker & Bassett
Tip	Semi-parametric distributional regression model	Conditional quantile regression
Sursa seminală	Rigby, 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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Denumiri alternative	Distributional Regression, Flexible Regression and Smoothing, GAMLSS Framework, Konum, Ölçek ve Şekil için Genelleştirilmiş Toplamlı Modeller	conditional quantile regression, regression quantiles, Kantil Regresyon
Înrudite	2	5
Rezumat	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 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.