Calibration of the empirical likelihood for semiparametric varying-coefficient partially linear models with diverging number of parameters

Year 2019, Volume: 48 Issue: 1, 230 - 241, 01.02.2019

Junwei He Xia Chen

Abstract

This article is concerned with the calibration of the empirical likelihood for semiparametric varying-coefficient partially linear models with diverging number of parameters.
However, there is always substantial lack-of-fit, when the empirical likelihood ratio is calibrated by a bias-corrected empirical likelihood, producing tests with type I errors much larger than nominal levels. So we consider an effective calibration method and study the asymptotic behavior of this bias-corrected empirical likelihood ratio function. Some simulation studies are conducted to illustrate our approach.

Keywords

Varying-coefficient partially linear models, Empirical likelihood, Bias correction, Asymptotic normality, Coverage accuracy

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