Translator Disclaimer
December, 1992 Profile Likelihood and Conditionally Parametric Models
Thomas A. Severini, Wing Hung Wong
Ann. Statist. 20(4): 1768-1802 (December, 1992). DOI: 10.1214/aos/1176348889

Abstract

In this paper, we outline a general approach to estimating the parametric component of a semiparametric model. For the case of a scalar parametric component, the method is based on the idea of first estimating a one-dimensional subproblem of the original problem that is least favorable in the sense of Stein. The likelihood function for the scalar parameter along this estimated subproblem may be viewed as a generalization of the profile likelihood for the problem. The scalar parameter is then estimated by maximizing this "generalized profile likelihood." This method of estimation is applied to a particular class of semiparametric models, where it is shown that the resulting estimator is asymptotically efficient.

Citation

Download Citation

Thomas A. Severini. Wing Hung Wong. "Profile Likelihood and Conditionally Parametric Models." Ann. Statist. 20 (4) 1768 - 1802, December, 1992. https://doi.org/10.1214/aos/1176348889

Information

Published: December, 1992
First available in Project Euclid: 12 April 2007

zbMATH: 0768.62015
MathSciNet: MR1193312
Digital Object Identifier: 10.1214/aos/1176348889

Subjects:
Primary: 62F35
Secondary: 62F10, 62G07

Rights: Copyright © 1992 Institute of Mathematical Statistics

JOURNAL ARTICLE
35 PAGES


SHARE
Vol.20 • No. 4 • December, 1992
Back to Top