Power Comparison of Tests for Extra-Variation of Counts Data

• 발행기관: 한국자료분석학회
• 저자명: 이재은(Jae Eun Lee) 이현영(Hyun Yung Lee) 정광모(Kwang Mo Jeong)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.17 No.3, 1165~1174쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2015.06.30

영문 초록

We formulate the extension of Poisson exponential family to incorporate the extra-variation of counts data. Assuming a generalized linear mixed model in which the natural parameter identifies a linear predictor including random effects, we discuss several tests for the Poisson assumption against the extra-variation model. As competitors of score test we suggest the likelihood ratio test and the Wald test. A fit of GLMM (generalized linear mixed model) with computational complexity is required in the latter two statistics but they have no serious obstacle in practical use because commonly used statistical packages provide procedures to fit GLMM. The tests have been applied to an example, and through a Monte Carlo study we also compare them in the respects of sizes and powers.

목차

1. Introduction

2. Formulating Extra-Variation of Counts via GLMM

3. Testing Extra-Variation of Counts

4. A Practical Example

5. Monte Carlo Study

6. Concluding Remarks

References

키워드

extra-variation score test likelihood ratio test Wald test Fisher information