Electronic Journal of Statistics

Empirical likelihood inference for partially time-varying coefficient errors-in-variables models

Guo-Liang Fan, Hong-Xia Xu, and Han-Ying Liang

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In this paper, the empirical likelihood inferences for partially time-varying coefficient errors-in-variables model with dependent observations are investigated. We propose an empirical log-likelihood ratio function for the regression parameters and show that its limiting distribution is a mixture of central chi-squared distributions. In order that the Wilks’ phenomenon holds, we construct an adjusted empirical log-likelihood ratio for the regression parameters. The adjusted empirical log-likelihood is shown to have a standard chi-squared limiting distribution. Simulations show that the proposed confidence regions have satisfactory performance.

Article information

Electron. J. Statist., Volume 6 (2012), 1040-1058.

First available in Project Euclid: 19 June 2012

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G15: Tolerance and confidence regions
Secondary: 62E20: Asymptotic distribution theory

Time-varying coefficient model errors-in-variables model empirical likelihood confidence region $\alpha $-mixing


Fan, Guo-Liang; Xu, Hong-Xia; Liang, Han-Ying. Empirical likelihood inference for partially time-varying coefficient errors-in-variables models. Electron. J. Statist. 6 (2012), 1040--1058. doi:10.1214/12-EJS701. https://projecteuclid.org/euclid.ejs/1340117521

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