TWO-STEP SPLINE ESTIMATING EQUATIONS FOR GENERALIZED ADDITIVE PARTIALLY LINEAR MODELS WITH LARGE CLUSTER SIZES

• 成果类型: Article
• 署名作者: Ma, Shujie
• 署名单位: University of California System; University of California Riverside
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS1056
• 发表日期: 2012
• 页码: 2943-2972
• 关键词: longitudinal/clustered data; Nonparametric Regression; polynomial spline; efficient estimation; local asymptotics

摘要

We propose a two-step estimating procedure for generalized additive partially linear models with clustered data using estimating equations. Our proposed method applies to the case that the number of observations per cluster is allowed to increase with the number of independent subjects. We establish oracle properties for the two-step estimator of each function component such that it performs as well as the univariate function estimator by assuming that the parametric vector and all other function components are known. Asymptotic distributions and consistency properties of the estimators are obtained. Finite-sample experiments with both simulated continuous and binary response variables confirm the asymptotic results. We illustrate the methods with an application to a U.S. unemployment data set.