QUASI-LIKELIHOOD ESTIMATION IN SEMIPARAMETRIC MODELS

• 成果类型: Article
• 署名作者: SEVERINI, TA; STANISWALIS, JG
• 署名单位: University of Texas System; University of Texas El Paso
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290852
• 发表日期: 1994
• 页码: 501-511
• 关键词: LONGITUDINAL DATA-ANALYSIS; linear-models; regression

摘要

Suppose the expected value of a response variable Y may be written h(Xbeta + gamma(T)) where X and T are covariates, each of which may be vector-valued, beta is an unknown parameter vector, gamma is an unknown smooth function, and h is a known function. In this article, we outline a method for estimating the parameter beta, gamma of this type of semiparametric model using a quasi-likelihood function. Algorithms for computing the estimates are given and the asymptotic distribution theory for the estimators is developed. The generalization of this approach to the case in which Y is a multivariate response is also considered. The methodology is illustrated on two data sets and the results of a small Monte Carlo study are presented.