Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data

• 成果类型: Article
• 署名作者: Zhao, Jiwei; Shao, Jun
• 署名单位: State University of New York (SUNY) System; University at Buffalo, SUNY; East China Normal University; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.983234
• 发表日期: 2015
• 页码: 1577-1590
• 关键词: data mechanism; nonresponse; estimators

摘要

We consider identifiability and estimation in a generalized linear model in which the response variable and some covariates have missing values and the missing data mechanism is nonignorable and unspecified. We adopt a pseudo-likelihood approach that makes use of an instrumental variable to help identifying unknown parameters in the presence of nonignorable missing data. Explicit conditions for the identifiability of parameters are given. Some asymptotic properties of the parameter estimators based on maximizing the pseudo-likelihood are established. Explicit asymptotic covariance matrix and its estimator are also derived in some cases. For the numerical maximization of the pseudo-likelihood, we develop a two-step iteration algorithm that decomposes a nonconcave maximization problem into two problems of maximizing concave functions. Some simulation results and an application to a dataset from cotton factory workers are also presented.