Electronic Journal of Statistics

Estimation and inference of error-prone covariate effect in the presence of confounding variables

Jianxuan Liu, Yanyuan Ma, Liping Zhu, and Raymond J. Carroll

Full-text: Open access

Abstract

We introduce a general single index semiparametric measurement error model for the case that the main covariate of interest is measured with error and modeled parametrically, and where there are many other variables also important to the modeling. We propose a semiparametric bias-correction approach to estimate the effect of the covariate of interest. The resultant estimators are shown to be root-$n$ consistent, asymptotically normal and locally efficient. Comprehensive simulations and an analysis of an empirical data set are performed to demonstrate the finite sample performance and the bias reduction of the locally efficient estimators.

Article information

Source
Electron. J. Statist., Volume 11, Number 1 (2017), 480-501.

Dates
Received: March 2015
First available in Project Euclid: 2 March 2017

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1488423805

Digital Object Identifier
doi:10.1214/17-EJS1242

Mathematical Reviews number (MathSciNet)
MR3619314

Zentralblatt MATH identifier
1359.62190

Subjects
Primary: 62H12: Estimation 62H15: Hypothesis testing
Secondary: 62F12: Asymptotic properties of estimators

Keywords
Confounding effect measurement error primary effect semiparametric efficiency single index model

Rights
Creative Commons Attribution 4.0 International License.

Citation

Liu, Jianxuan; Ma, Yanyuan; Zhu, Liping; Carroll, Raymond J. Estimation and inference of error-prone covariate effect in the presence of confounding variables. Electron. J. Statist. 11 (2017), no. 1, 480--501. doi:10.1214/17-EJS1242. https://projecteuclid.org/euclid.ejs/1488423805


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