TITLE

Efficient estimation of a varying-coefficient partially linear binary regression model

AUTHOR(S)
Tao Hu; Heng Jian Cui
PUB. DATE
November 2010
SOURCE
Acta Mathematica Sinica;Nov2010, Vol. 26 Issue 11, p2179
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This article considers a semiparametric varying-coefficient partially linear binary regression model. The semiparametric varying-coefficient partially linear regression binary model which is a generalization of binary regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. One of our main objects is to estimate nonparametric component and the unknown parameters simultaneously. It is easier to compute, and the required computation burden is much less than that of the existing two-stage estimation method. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained, and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are carried out to investigate the performance of the proposed method.
ACCESSION #
54087303

 

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