Σύγκριση μεθόδων

	Άρτιο (Robust) Probit Μοντέλο	Γενικευμένο Γραμμικό Μοντέλο (GLM)
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1934 / 1980s	1972
Δημιουργός	Hal White (sandwich variance); classical probit by Bliss (1934)	John A. Nelder & Robert W. M. Wedderburn
Τύπος	Binary outcome regression with robust inference	Regression framework
Θεμελιώδης πηγή	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
Εναλλακτικές ονομασίες	probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit	GLM, generalized regression, exponential family regression, link-function model
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.	The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.