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Journals / Hacettepe Journal of Mathematics and Statistics / 2026 / Volume: 48 Issue: 1

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

Junwei He
Xia Chen
Pages
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Publication Date
2019-02-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Some simulation studies are conducted to illustrate our approach.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This article is concerned with the calibration of the empirical likelihood for semiparametric varying-coefficient partially linear models with diverging number of parameters.
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Key Findings & Data: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.
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Conclusion & Impact:Some simulation studies are conducted to illustrate our approach.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Varying-coefficient partially linear models · Empirical likelihood · Bias correction

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.

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Cite This Article

He, J., Chen, X. (2019). Calibration of the empirical likelihood for semiparametric varying-coefficient partially linear models with diverging number of parameters. Hacettepe Journal of Mathematics and Statistics, Volume: 48 Issue: 1. https://ajindex.com/articles/1402846

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