Vertaile menetelmiä

	Robust Probit -malli	Logistinen regressio
Tieteenala	Tilastotiede	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Regression model	Process / pipeline
Syntyvuosi	1934 / 1980s	1958
Kehittäjä	Hal White (sandwich variance); classical probit by Bliss (1934)	David Roxbee Cox
Tyyppi	Binary outcome regression with robust inference	Method
Alkuperäislähde	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Rinnakkaisnimet	probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit	logit model, binomial logistic regression, LR
Liittyvät	4	3
Tiivistelmä	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.