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| Model Robust Probit× | Regresja odporna× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1934 / 1980s | 1964 |
| Twórca≠ | Hal White (sandwich variance); classical probit by Bliss (1934) | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Typ≠ | Binary outcome regression with robust inference | Regression with outlier resistance |
| Źródło pierwotne≠ | 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. DOI ↗ |
| Inne nazwy | 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 |
| Pokrewne≠ | 4 | 6 |
| Podsumowanie≠ | 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. |
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