A SEMIPARAMETRIC MODEL FOR CLUSTER DATA

• 成果类型: Article
• 署名作者: Zhang, Wenyang; Fan, Jianqing; Sun, Yan
• 署名单位: University of Bath; Princeton University; Shanghai University of Finance & Economics
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/08-AOS662
• 发表日期: 2009
• 页码: 2377-2408
• 关键词: varying-coefficient models; longitudinal data; Nonparametric Regression; confidence bands; inference; selection; spline

摘要

In the analysis of cluster data, the regression coefficients are frequently assumed to be the same across all clusters. This hampers the ability to Study the varying impacts of factors on each cluster. In this paper, a semiparametric model is introduced to account for varying impacts of factors over clusters by using cluster-level covariates. It achieves the parsimony of parametrization and allows the explorations of nonlinear interactions. The random effect ill the semiparametric model also accounts for within-cluster correlation. Local. linear-based estimation procedure is proposed for estimating functional coefficients, residual variance and within-cluster correlation matrix. The asymptotic properties of the proposed estimators are established, and the method for constructing Simultaneous confidence bands are proposed and studied. In addition, relevant hypothesis testing problems ire addressed. Simulation studies are carried out to demonstrate the methodological power of the proposed methods in the finite sample. The proposed model and methods are used to analyse the second birth interval in Bangladesh, leading to some interesting findings.