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February 1997 Semiparametric inference in a partial linear model
P. K. Bhattacharya, Peng-Liang Zhao
Ann. Statist. 25(1): 244-262 (February 1997). DOI: 10.1214/aos/1034276628

Abstract

In a partial linear model, the dependence of a response variate Y on covariates (W, X$ is given by $$Y = W \beta + \eta(X) + \mathscr{E}$$ where $\mathscr{E}$ is independent of $(W, X)$ with densities g and f, respectively. In this paper an asymptotically efficient estimator of $\beta$ is constructed solely under mild smoothness assumptions on the unknown $\eta$, f and g, thereby removing the assumption of finite residual variance on which all least-squares-type estimators available in the literature are based.

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P. K. Bhattacharya. Peng-Liang Zhao. "Semiparametric inference in a partial linear model." Ann. Statist. 25 (1) 244 - 262, February 1997. https://doi.org/10.1214/aos/1034276628

Information

Published: February 1997
First available in Project Euclid: 10 October 2002

zbMATH: 0869.62050
MathSciNet: MR1429924
Digital Object Identifier: 10.1214/aos/1034276628

Subjects:
Primary: 62F12, 62J99
Secondary: 62G07

Rights: Copyright © 1997 Institute of Mathematical Statistics

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Vol.25 • No. 1 • February 1997
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