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| Robust Probit Model× | Robust logistisk regression× | |
|---|---|---|
| Fagområde | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 1934 / 1980s | 2001 |
| Ophavsperson≠ | Hal White (sandwich variance); classical probit by Bliss (1934) | Cantoni & Ronchetti (2001); Bondell (2008) |
| Type≠ | Binary outcome regression with robust inference | Robust generalized linear model (binary outcome) |
| Oprindelig kilde≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗ |
| Aliasser | probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit | robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon |
| Relaterede≠ | 4 | 5 |
| Resumé≠ | 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 Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008). |
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