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| 강건 영과대 모형× | 로버스트 포아송 회귀× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1990s–2000s | 2004 |
| 창시자≠ | Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors | Guangyong Zou |
| 유형≠ | Robust count regression with excess zeros | GLM with robust variance |
| 원전≠ | Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25. DOI ↗ | Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706. DOI ↗ |
| 별칭 | robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson | modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson |
| 관련 | 5 | 5 |
| 요약≠ | 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 Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression. |
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