Uporedite metode

	Робусна дискриминантна анализа	Heteroscedasticity-Robust (HC) Standard Errors
Oblast	Statistika	Statistika
Porodica	Regression model	Regression model
Godina nastanka	1997	1980
Tvorac	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)	Eicker; Huber; White (1980); MacKinnon & White (1985)
Tip	Robust classification / discriminant analysis	Robust covariance estimator for linear regression
Temeljni izvor	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.
Drugi nazivi	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
Srodne	5	5
Sažetak	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.