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2011 Estimation via corrected scores in general semiparametric regression models with error-prone covariates
Arnab Maity, Tatiyana V. Apanasovich
Electron. J. Statist. 5: 1424-1449 (2011). DOI: 10.1214/11-EJS647

Abstract

This paper considers the problem of estimation in a general semiparametric regression model when error-prone covariates are modeled parametrically while covariates measured without error are modeled nonparametrically. To account for the effects of measurement error, we apply a correction to a criterion function. The specific form of the correction proposed allows Monte Carlo simulations in problems for which the direct calculation of a corrected criterion is difficult. Therefore, in contrast to methods that require solving integral equations of possibly multiple dimensions, as in the case of multiple error-prone covariates, we propose methodology which offers a simple implementation. The resulting methods are functional, they make no assumptions about the distribution of the mismeasured covariates. We utilize profile kernel and backfitting estimation methods and derive the asymptotic distribution of the resulting estimators. Through numerical studies we demonstrate the applicability of proposed methods to Poisson, logistic and multivariate Gaussian partially linear models. We show that the performance of our methods is similar to a computationally demanding alternative. Finally, we demonstrate the practical value of our methods when applied to Nevada Test Site (NTS) Thyroid Disease Study data.

Citation

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Arnab Maity. Tatiyana V. Apanasovich. "Estimation via corrected scores in general semiparametric regression models with error-prone covariates." Electron. J. Statist. 5 1424 - 1449, 2011. https://doi.org/10.1214/11-EJS647

Information

Published: 2011
First available in Project Euclid: 4 November 2011

zbMATH: 1271.62087
MathSciNet: MR2851685
Digital Object Identifier: 10.1214/11-EJS647

Subjects:
Primary: 62G08
Secondary: 62G20

Rights: Copyright © 2011 The Institute of Mathematical Statistics and the Bernoulli Society

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