方法对比

	LOESS / LOWESS局部回归	多元自适应回归样条 (MARS)	多项式回归
领域	机器学习	机器学习	统计学
方法族	Machine learning	Machine learning	Regression model
起源年份	1979	1991	2012
提出者	William S. Cleveland	Jerome H. Friedman	Montgomery, Peck & Vining (textbook treatment); classical least squares
类型	Local nonparametric regression smoother	Adaptive piecewise-linear regression	Linear regression in transformed predictors
开创性文献	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
别名	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	polynomial least squares, curvilinear regression, Polinom Regresyonu
相关	3	4	4
摘要	LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.	Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the data.	Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.