Covariance Estimates of GLMs under Model Misspecification

• 발행기관: 한국자료분석학회
• 저자명: Heewon Yoon Kwang Mo Jeong
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.20 No.5, 2177~2187쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2018.10.31

영문 초록

In analyzing data set we usually assume a tentative model that seems to be valid. But the assumed model may be incorrect in respect of distributional assumption, regression relationship, and so on. The incorrect model usually causes larger variance estimates than the naive variance estimates that are given as the inverse of Hessian matrix. In this paper, we discuss robust variance estimates that are more valid in the presence of model misspecification. Through a Monte Carlo study we investigate the improvement of robust variance estimates against the naive estimates in respect of attaining nominal coverage probabilities for the regression coefficients of generalized linear models. We consider logistic GLMs for binomial responses versus beta-binomial or zero-inflated binomial responses, and also loglinear GLMs for Poisson counts versus negative binomial or zero-inflated counts responses. In the presence of model misspecification, the robust covariance estimator appears to be desirable in attaining nominal significance level.

목차

1. Introduction

2. Pseudo Maximum Likelihood Estimation in Generalized Linear Models

3. Misspecified Response Distribution in Generalized Linear Models

4. A Monte Carlo Study

5. Concluding Remarks

References

키워드

Misspecified model Kullback-Leibler information Pseudo-MLE Robust covariance estimator Naive covariance estimator