Two-step likelihood estimation procedure for varying-coefficient models

被引：22

作者：

Cai, ZW ^{[1
]}

机构：

[1] Univ N Carolina, Charlotte, NC 28223 USA

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2002年 / 82卷 / 01期

基金：

美国国家科学基金会;

关键词：

asymptotic normality; generalized linear model; local polynomial fitting; mean squared errors; optimal convergent rate; varying-coefficient model;

D O I：

10.1006/jmva.2001.2013

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

One of the advantages for the varying-coefficient model is to allow the coefficients to vary as smooth functions of other variables and the model can be estimated easily through a simple local quasi-likelihood method. This leads to a simple one-step estimation procedure. We show that such a one-step method cannot be optimal when some coefficient functions possess different degrees of smoothness. This drawback can be attenuated by using a two-step estimation approach. The asymptotic normality and mean-squared errors of the two-step method are obtained and it is also shown that the two-step estimation not only achieves the optimal convergent rate but also shares the same optimality as the ideal case where the other coefficient functions were known. A numerical study is carried out to illustrate the two-step method. (C) 2001 Elsevier Science (USA).

引用

页码：189 / 209

页数：21