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| 稳健零膨胀模型× | 稳健负二项回归× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1990s–2000s | 2000s–2011 |
| 提出者≠ | Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors | Hilbe, J. M.; Zeileis, A. et al. |
| 类型≠ | Robust count regression with excess zeros | Count regression with robust inference |
| 开创性文献≠ | Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158 |
| 别名 | robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson | robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression |
| 相关≠ | 5 | 6 |
| 摘要≠ | The robust zero-inflated model extends standard zero-inflated count regression — which handles excess zeros via a mixture of a point mass at zero and a count distribution — by replacing or supplementing classical maximum likelihood with robust estimation techniques (M-estimators, sandwich standard errors) that protect against the distorting influence of outlying observations. | Robust Negative Binomial Regression models overdispersed count outcomes using the negative binomial distribution while protecting coefficient inference against misspecification of the variance function. It pairs maximum-likelihood estimation of the mean and dispersion parameters with sandwich (Huber-White) standard errors, yielding valid tests even when the assumed variance structure is only approximately correct. |
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