Two-step likelihood estimation procedure for varying-coefficient models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:82

Article

Cai, ZW

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

DOI  :  10.1006/jmva.2001.2013

来源: Elsevier

【 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).

【 授权许可】

Free