Jackknife empirical likelihood for the error variance in linear models. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Lin, Hui-Ling; Li, Zhouping; Wang, Dongliang; Zhao, Yichuan
• Is Part Of: Journal of nonparametric statistics. Volume 29:Issue 2(2017)
• Page Start: 151
• Page End: 166
• Publication Date: 2017-04-03
• Subjects: Confidence interval -- empirical likelihood -- error variance -- jackknife empirical likelihood -- linear regression; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2017.1285028
• Languages: English
• ISSNs: 1048-5252

Abstract

ABSTRACT: Variance estimation is a fundamental yet important problem in statistical modelling. In this paper, we propose jackknife empirical likelihood (JEL) methods for the error variance in a linear regression model. We prove that the JEL ratio converges to the standard chi-squared distribution. The asymptotic chi-squared properties for the adjusted JEL and extended JEL estimators are also established. Extensive simulation studies to compare the new JEL methods with the standard method in terms of coverage probability and interval length are conducted, and the simulation results show that our proposed JEL methods perform better than the standard method. We also illustrate the proposed methods using two real data sets.