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| 강건 프로빗 모형× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 연구 통계 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 1934 / 1980s | 1958 |
| 창시자≠ | Hal White (sandwich variance); classical probit by Bliss (1934) | David Roxbee Cox |
| 유형≠ | Binary outcome regression with robust inference | Method |
| 원전≠ | 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 ↗ |
| 별칭≠ | probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit | logit model, binomial logistic regression, LR |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. |
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