Compara mètodes

	Anàlisi de Components Principals Robusta (RPCA)	Regressió robusta
Camp	Estadística	Estadística
Família	Regression model	Regression model
Any d'origen	2011	1964
Autor original	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Tipus	Robust dimensionality reduction / matrix decomposition	Regression with outlier resistance
Font seminal	Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Àlies	RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA)	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Relacionats	3	6
Resum	Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.