Порівняння методів

	Локальна регресія LOESS / LOWESS	Узагальнена адитивна модель (GAM)	Поліноміальна регресія	Регресійні та згладжувальні сплайни
Галузь	Машинне навчання	Машинне навчання	Статистика	Машинне навчання
Родина	Machine learning	Machine learning	Regression model	Machine learning
Рік появи	1979	1986	2012	1996
Автор методу	William S. Cleveland	Trevor Hastie & Robert Tibshirani	Montgomery, Peck & Vining (textbook treatment); classical least squares	Spline regression literature; P-splines by Eilers & Marx
Тип	Local nonparametric regression smoother	Semi-parametric additive regression model	Linear regression in transformed predictors	Piecewise-polynomial nonparametric regression
Основоположне джерело	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	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.
Інші назви	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	polynomial least squares, curvilinear regression, Polinom Regresyonu	splines, cubic splines, natural splines, smoothing splines
Пов'язані	3	4	4	4
Підсумок	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.	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.	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.