Σύγκριση μεθόδων

	Άρτιο (Robust) Probit Μοντέλο	Robust Regression
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1934 / 1980s	1964
Δημιουργός	Hal White (sandwich variance); classical probit by Bliss (1934)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Τύπος	Binary outcome regression with robust inference	Regression with outlier resistance
Θεμελιώδης πηγή	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Εναλλακτικές ονομασίες	probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Συναφείς	4	6
Σύνοψη	The Robust Probit Model estimates the probability of a binary outcome using the probit link function while protecting inference from misspecification of the error distribution or heteroscedasticity. Coefficients are obtained via maximum likelihood; standard errors are then replaced by the sandwich (Huber-White) estimator, which remains consistent even when the assumed error variance is incorrect.	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.