Information Ratio Test for Model Misspecification in Quasi-Likelihood Inference

• 成果类型: Article
• 署名作者: Zhou, Qian M.; Song, Peter X. -K.; Thompson, Mary E.
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; University of Michigan System; University of Michigan; University of Waterloo
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2011.645785
• 发表日期: 2012
• 页码: 205-213
• 关键词: estimating equations; regression; bootstrap; heteroscedasticity; overdispersion; Poisson

摘要

In this article, we focus on the circumstances in quasi-likelihood inference that the estimation accuracy of mean structure parameters is guaranteed by correct specification of the first moment, but the estimation efficiency could be diminished due to misspecification of the second moment. We propose an information ratio (IR) statistic to test for model misspecification of the variance/covariance structure through a comparison between two forms of information matrix: the negative sensitivity matrix and the variability matrix. We establish asymptotic distributions of the proposed IR test statistics. We also suggest an approximation to the asymptotic distribution of the IR statistic via a perturbation resampling method. Moreover, we propose a selection criterion based on the IR test to select the best fitting variance/covariance structure from a class of candidates. Through simulation studies, it is shown that the IR statistic provides a powerful statistical tool to detect different scenarios of misspecification of the variance/covariance structures. In addition, the IR test as well as the proposed model selection procedure shows substantial improvement over some of the existing statistical methods. The IR-based model selection procedure is illustrated by analyzing the Madras Longitudinal Schizophrenia data. Appendices are included in the supplemental materials, which are available online.