Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo Probit Robusto× | Modelo Lineal Generalizado (GLM)× | |
|---|---|---|
| Campo | Estadística | Estadística |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1934 / 1980s | 1972 |
| Autor original≠ | Hal White (sandwich variance); classical probit by Bliss (1934) | John A. Nelder & Robert W. M. Wedderburn |
| Tipo≠ | Binary outcome regression with robust inference | Regression framework |
| Fuente seminal≠ | 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. DOI ↗ |
| Alias | probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit | GLM, generalized regression, exponential family regression, link-function model |
| Relacionados≠ | 4 | 6 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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