Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo Probit Robusto× | Regresión Logística× | |
|---|---|---|
| Campo≠ | Estadística | Estadística para la investigación |
| Familia≠ | Regression model | Process / pipeline |
| Año de origen≠ | 1934 / 1980s | 1958 |
| Autor original≠ | Hal White (sandwich variance); classical probit by Bliss (1934) | David Roxbee Cox |
| Tipo≠ | Binary outcome regression with robust inference | Method |
| Fuente seminal≠ | 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. DOI ↗ |
| Alias≠ | probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit | logit model, binomial logistic regression, LR |
| Relacionados≠ | 4 | 3 |
| 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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