Quasi-likelihood estimation in semiparametric models

Thomas A. Severini, Joan G. Staniswalis

Research output: Contribution to journalArticlepeer-review

264 Scopus citations

Abstract

Suppose the expected value of a response variable Y may be written h(Xβ +γ(T)) where X and T are covariates, each of which may be vector-valued, β is an unknown parameter vector, γ is an unknown smooth function, and h is a known function. In this article, we outline a method for estimating the parameter β, γ 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.

Original languageEnglish (US)
Pages (from-to)501-511
Number of pages11
JournalJournal of the American Statistical Association
Volume89
Issue number426
DOIs
StatePublished - Jun 1994

Keywords

  • Generalized linear models
  • Multivariate regression
  • Nonparametric regression
  • Partially linear models
  • Smoothing

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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