Electronic Journal of Statistics

Model averaging for varying-coefficient partially linear measurement error models

Haiying Wang, Guohua Zou, and Alan T. K. Wan

Full-text: Open access

Abstract

In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of model average estimators under parametric models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varying-coefficient partially linear measurement error model. Within this context, we develop a model averaging scheme for the unknowns, derive the model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the model average estimators are asymptotically the same as those obtained under the full model. A simulation study examines the finite sample performance of the model average estimators, and a real data analysis illustrates the application of the method in practice.

Article information

Source
Electron. J. Statist., Volume 6 (2012), 1017-1039.

Dates
First available in Project Euclid: 11 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1339373538

Digital Object Identifier
doi:10.1214/12-EJS704

Mathematical Reviews number (MathSciNet)
MR2988437

Zentralblatt MATH identifier
1281.62054

Subjects
Primary: 62E20: Asymptotic distribution theory
Secondary: 62F10: Point estimation 62F12: Asymptotic properties of estimators

Keywords
Asymptotic equivalence measurement errors model averaging model selection semi-parametric models

Citation

Wang, Haiying; Zou, Guohua; Wan, Alan T. K. Model averaging for varying-coefficient partially linear measurement error models. Electron. J. Statist. 6 (2012), 1017--1039. doi:10.1214/12-EJS704. https://projecteuclid.org/euclid.ejs/1339373538


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