Porovnať metódy

	Zovšeobecnené aditívne modely pre polohu, škálu a tvar (GAMLSS)	Zovšeobecnený aditívny model (GAM)
Odbor	Štatistika	Strojové učenie
Rodina	Regression model	Machine learning
Rok vzniku	2005	1986
Tvorca	Robert Rigby & Mikis Stasinopoulos	Trevor Hastie & Robert Tibshirani
Typ	Semi-parametric distributional regression model	Semi-parametric additive regression model
Pôvodný zdroj	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.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.
Ďalšie názvy	Distributional Regression, Flexible Regression and Smoothing, GAMLSS Framework, Konum, Ölçek ve Şekil için Genelleştirilmiş Toplamlı Modeller	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
Príbuzné	2	4
Zhrnutie	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.	A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.