TY - JOUR

T1 - Quasi-likelihood estimation in semiparametric models

AU - Severini, Thomas A.

AU - Staniswalis, Joan G.

N1 - Funding Information:
* Thomas A. Severini is Assistant Professor, Department of Statistics, Northwestern University, Evanston, IL 60208. Joan G. Staniswalis is Associate Professor, Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968. The work of Staniswalis was supported by National Science Foundation Grant DMS 9 12 1555 and National Institutes of Health Grant S06GM08012-22. The authors thank the referees and associate editor for useful comments.

PY - 1994/6

Y1 - 1994/6

N2 - 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.

AB - 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.

KW - Generalized linear models

KW - Multivariate regression

KW - Nonparametric regression

KW - Partially linear models

KW - Smoothing

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U2 - 10.1080/01621459.1994.10476774

DO - 10.1080/01621459.1994.10476774

M3 - Article

AN - SCOPUS:84950442031

VL - 89

SP - 501

EP - 511

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 426

ER -