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A Study on the Power Comparison between Logistic Regression and Offset Poisson Regression for Binary Data
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  • A Study on the Power Comparison between Logistic Regression and Offset Poisson Regression for Binary Data
  • A Study on the Power Comparison between Logistic Regression and Offset Poisson Regression for Binary Data
저자명
Kim. Dae-Youb,Park. Heung-Sun
간행물명
한국통계학회 논문집
권/호정보
2012년|19권 4호|pp.537-546 (10 pages)
발행정보
한국통계학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

In this paper, for analyzing binary data, Poisson regression with offset and logistic regression are compared with respect to the power via simulations. Poisson distribution can be used as an approximation of binomial distribution when n is large and p is small; however, we investigate if the same conditions can be held for the power of significant tests between logistic regression and offset poisson regression. The result is that when offset size is large for rare events offset poisson regression has a similar power to logistic regression, but it has an acceptable power even with a moderate prevalence rate. However, with a small offset size (< 10), offset poisson regression should be used with caution for rare events or common events. These results would be good guidelines for users who want to use offset poisson regression models for binary data.