The Annals of Statistics

Asymptotically Efficient Estimation in Semiparametric Generalized Linear Models

Hung Chen

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Abstract

We use the method of maximum likelihood and regression splines to derive estimates of the parametric and nonparametric components of semiparametric generalized linear models. The resulting estimators of both components are shown to be consistent. Also, the asymptotic theory for the estimator of the parametric component is derived, indicating that the parametric component can be estimated efficiently without under-smoothing the nonparametric component.

Article information

Source
Ann. Statist., Volume 23, Number 4 (1995), 1102-1129.

Dates
First available in Project Euclid: 11 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176324700

Digital Object Identifier
doi:10.1214/aos/1176324700

Mathematical Reviews number (MathSciNet)
MR1353497

Zentralblatt MATH identifier
0838.62024

JSTOR
links.jstor.org

Subjects
Primary: 62G07: Density estimation
Secondary: 62F12: Asymptotic properties of estimators 62J12: Generalized linear models

Keywords
Partial spline model generalized linear model semiparametric regression model maximum likelihood estimator

Citation

Chen, Hung. Asymptotically Efficient Estimation in Semiparametric Generalized Linear Models. Ann. Statist. 23 (1995), no. 4, 1102--1129. doi:10.1214/aos/1176324700. https://projecteuclid.org/euclid.aos/1176324700


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