FLEXIBLE GENERALIZED VARYING COEFFICIENT REGRESSION MODELS

• 成果类型: Article
• 署名作者: Lee, Young K.; Mammen, Enno; Park, Byeong U.
• 署名单位: Kangwon National University; University of Mannheim; Seoul National University (SNU)
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS1026
• 发表日期: 2012
• 页码: 1906-1933
• 关键词: Longitudinal Data; ADDITIVE-MODELS; polynomial spline; linear-models; time-series; selection; DYNAMICS

摘要

This paper studies a very flexible model that can be used widely to analyze the relation between a response and multiple covariates. The model is nonparametric, yet renders easy interpretation for the effects of the covariates. The model accommodates both continuous and discrete random variables for the response and covariates. It is quite flexible to cover the generalized varying coefficient models and the generalized additive models as special cases. Under a weak condition we give a general theorem that the problem of estimating the multivariate mean function is equivalent to that of estimating its univariate component functions. We discuss implications of the theorem for sieve and penalized least squares estimators, and then investigate the outcomes in full details for a kernel-type estimator. The kernel estimator is given as a solution of a system of nonlinear integral equations. We provide an iterative algorithm to solve the system of equations and discuss the theoretical properties of the estimator and the algorithm. Finally, we give simulation results.