Linganisha mbinu

	Mifumo Additive ya Jumla kwa Mahali, Kiwango na Umbo (GAMLSS)	Generalized Additive Model (GAM)	Regression ya Kiasi (Quantile Regression)
Nyanja	Takwimu	Ujifunzaji wa Mashine	Ekonometriki
Familia	Regression model	Machine learning	Regression model
Mwaka wa asili	2005	1986	1978
Mwanzilishi	Robert Rigby & Mikis Stasinopoulos	Trevor Hastie & Robert Tibshirani	Koenker & Bassett
Aina	Semi-parametric distributional regression model	Semi-parametric additive regression model	Conditional quantile regression
Chanzo asilia	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Majina mbadala	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	conditional quantile regression, regression quantiles, Kantil Regresyon
Zinazohusiana	2	4	5
Muhtasari	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.	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.