Bandingkan kaedah

	Model Additif Umum untuk Lokasi, Skala dan Bentuk (GAMLSS)	Model Aditif Generalized (GAM)
Bidang	Statistik	Pembelajaran Mesin
Keluarga	Regression model	Machine learning
Tahun asal	2005	1986
Pengasas	Robert Rigby & Mikis Stasinopoulos	Trevor Hastie & Robert Tibshirani
Jenis	Semi-parametric distributional regression model	Semi-parametric additive regression model
Sumber perintis	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.
Alias	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
Berkaitan	2	4
Ringkasan	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.