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| 贝叶斯 Tobit 模型× | 零膨胀模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1958 (classical); 1992 (Bayesian formulation) | 1992 |
| 提出者≠ | James Tobin (classical Tobit, 1958); Siddhartha Chib (Bayesian Tobit, 1992) | Diane Lambert |
| 类型≠ | Bayesian censored/limited-dependent-variable regression | Count regression with excess zeros |
| 开创性文献≠ | Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36. DOI ↗ | Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. DOI ↗ |
| 别名 | Bayesian censored regression, Bayesian Type I Tobit, Bayesian truncated regression, Tobit with priors | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts. |
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