Estimation in partially linear models with missing covariates

• 成果类型: Article
• 署名作者: Liang, H; Wang, SJ; Robins, JM; Carroll, RJ
• 署名单位: Texas A&M University System; Texas A&M University College Station; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000000421
• 发表日期: 2004
• 页码: 357-367
• 关键词: regression

摘要

The partially linear model Y = X(T)beta + v(Z) + epsilon has been studied extensively when data are completely observed. In this article, we consider the case where the covariate X is sometimes missing, with missingness probability pi depending on (Y, Z). New methods are developed for estimating and v(.). Our methods are shown to outperform asymptotically methods based only on the complete data. Asymptotic efficiency is discussed. and the semiparametric efficient score function is derived. Justification of the use of the nonparametric bootstrap in this context is sketched. The proposed estimators are extended to a working independence analysis of longitudinal/clustered data and applied to analyze an AIDS clinical trial dataset. The results of a simulation experiment are also given to illustrate our approach.