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| 베이즈 토빗 모형× | 베이지안 프로빗 모형× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1958 (classical); 1992 (Bayesian formulation) | 1993 |
| 창시자≠ | James Tobin (classical Tobit, 1958); Siddhartha Chib (Bayesian Tobit, 1992) | Albert & Chib (data augmentation formulation) |
| 유형≠ | Bayesian censored/limited-dependent-variable regression | Binary regression (Bayesian) |
| 원전≠ | Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36. DOI ↗ | Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679. DOI ↗ |
| 별칭 | Bayesian censored regression, Bayesian Type I Tobit, Bayesian truncated regression, Tobit with priors | Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit |
| 관련≠ | 5 | 6 |
| 요약≠ | The Bayesian Tobit model extends Tobin's censored regression framework by replacing maximum-likelihood point estimates with a full posterior distribution over regression coefficients and error variance. By embedding Gibbs sampling with data augmentation, it produces credible intervals, handles small censored samples gracefully, and naturally incorporates prior knowledge about effect sizes. | The Bayesian Probit model is a binary regression method that models the probability of a binary outcome using the normal CDF (probit link) within a Bayesian framework. It assigns prior distributions to regression coefficients and updates them with observed data, yielding a full posterior distribution rather than a single point estimate. The Albert-Chib data-augmentation algorithm makes posterior sampling computationally efficient via Gibbs sampling. |
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