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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


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.


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Thomas A. Severini. Wing Hung Wong. "Profile Likelihood and Conditionally Parametric Models." Ann. Statist. 20 (4) 1768 - 1802, December, 1992.


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

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

Primary: 62F35
Secondary: 62F10, 62G07

Rights: Copyright © 1992 Institute of Mathematical Statistics


Vol.20 • No. 4 • December, 1992
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