Functional-coefficient partially linear regression model

doi:10.1016/j.jmva.2007.03.003

CSpace

	Functional-coefficient partially linear regression model
	Wong, Heung 1; Zhang, Riquan 2,3; Ip, Wai-cheung 1; Li, Guoying 4
	2008-02-01
发表期刊	JOURNAL OF MULTIVARIATE ANALYSIS
ISSN	0047-259X
卷号	99 期号:2 页码:278-305
摘要	In this paper, the functional-coefficient partially linear regression (FCPLR) model is proposed by combining nonparametric and functional-coefficient regression (FCR) model. It includes the FCR model and the nonparametric regression (NPR) model as its special cases. It is also a generalization of the partially linear regression (PLR) model obtained by replacing the parameters in the PLR model with some functions of the covariates. The local linear technique and the integrated method are employed to give initial estimators of all functions in the FCPLR model. These initial estimators are asymptotically normal. The initial estimator of the constant part function shares the same bias as the local linear estimator of this function in the univariate nonparametric model, but the variance of the former is bigger than that of the latter. Similarly, initial estimators of every coefficient function share the same bias as the local linear estimates in the univariate FCR model, but the variance of the former is bigger than that of the latter. To decrease the variance of the initial estimates, a one-step back-fitting technique is used to obtain the improved estimators of all functions. The improved estimator of the constant part function has the same asymptotic normality property as the local linear nonparametric regression for univariate data. The improved estimators of the coefficient functions have the same asymptotic normality properties as the local linear estimates in FCR model. The bandwidths and the smoothing variables are selected by a data-driven method. Both simulated and real data examples related to nonlinear time series modeling are used to illustrate the applications of the FCPLR model. (C) 2007 Elsevier Inc. All rights reserved.
关键词	Back-fitting technique Functional-coefficient model Local linear polynomial technique Nonlinear time series
DOI	10.1016/j.jmva.2007.03.003
语种	英语
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000264679600006
出版者	ELSEVIER INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/5464
专题	中国科学院数学与系统科学研究院
通讯作者	Wong, Heung
作者单位	1.Hong Kong Polytech Univ, Dept Math Appl, Kowloon, Hong Kong, Peoples R China 2.Shanxi Datong Univ, Dept Math, Datong 037009, Shanxi, Peoples R China 3.E China Normal Univ, Dept Stat, Shanghai 200062, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wong, Heung,Zhang, Riquan,Ip, Wai-cheung,et al. Functional-coefficient partially linear regression model[J]. JOURNAL OF MULTIVARIATE ANALYSIS,2008,99(2):278-305.
APA	Wong, Heung,Zhang, Riquan,Ip, Wai-cheung,&Li, Guoying.(2008).Functional-coefficient partially linear regression model.JOURNAL OF MULTIVARIATE ANALYSIS,99(2),278-305.
MLA	Wong, Heung,et al."Functional-coefficient partially linear regression model".JOURNAL OF MULTIVARIATE ANALYSIS 99.2(2008):278-305.