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| 贝叶斯零膨胀模型× | 泊松回归与负二项回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1992–2006 | 1998 |
| 提出者≠ | Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| 类型≠ | Bayesian count regression | Generalized linear model for count data |
| 开创性文献≠ | Ghosh, S. K., Mukhopadhyay, P., & Lu, J.-C. (2006). Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4), 1360–1375. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| 别名 | Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| 相关≠ | 5 | 4 |
| 摘要≠ | The Bayesian zero-inflated model handles count data with excess zeros by combining a binary component — identifying structural zeros — with a count component (Poisson or negative binomial) for the remaining counts. Bayesian inference via MCMC provides full posterior distributions for all parameters, enabling principled uncertainty quantification and regularisation through priors. | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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