Confronta i metodi

	Regressione locale LOESS / LOWESS	Regressione polinomiale	Regression and Smoothing Splines
Campo	Apprendimento automatico	Statistica	Apprendimento automatico
Famiglia	Machine learning	Regression model	Machine learning
Anno di origine	1979	2012	1996
Ideatore	William S. Cleveland	Montgomery, Peck & Vining (textbook treatment); classical least squares	Spline regression literature; P-splines by Eilers & Marx
Tipo	Local nonparametric regression smoother	Linear regression in transformed predictors	Piecewise-polynomial nonparametric regression
Fonte seminale	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.
Alias	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	polynomial least squares, curvilinear regression, Polinom Regresyonu	splines, cubic splines, natural splines, smoothing splines
Correlati	3	4	4
Sintesi	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.	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.	Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models.