Módszerek összehasonlítása

	Robuszt diszkriminanciaanalízis	Robuszt standard errorok heteroszkedaszticitás esetén (HC)
Tudományterület	Statisztika	Statisztika
Módszercsalád	Regression model	Regression model
Keletkezés éve	1997	1980
Megalkotó	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)	Eicker; Huber; White (1980); MacKinnon & White (1985)
Típus	Robust classification / discriminant analysis	Robust covariance estimator for linear regression
Alapmű	Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.	White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.
Alternatív nevek	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors
Kapcsolódó	5	5
Összefoglaló	Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001).	Heteroscedasticity-robust standard errors are a correction to the covariance matrix of an OLS regression that yields valid inference when the error variance is not constant. Introduced by Halbert White in 1980 and refined into the finite-sample variants HC1-HC4 by MacKinnon and White in 1985, they leave the coefficient estimates unchanged but rebuild the standard errors so that t and F tests remain trustworthy under heteroscedasticity.