Date of Award
Summer 7-25-2013
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
First Advisor
Zhao, Yichuan
Abstract
The variance is the measure of spread from the center. Therefore, how to accurately estimate variance has always been an important topic in recent years. In this paper, we consider a linear regression model which is the most popular model in practice. We use jackknife empirical likelihood method to obtain the interval estimate of variance in the regression model. The proposed jackknife empirical likelihood ratio converges to the standard chi-squared distribution. The simulation study is carried out to compare the jackknife empirical likelihood method and standard method in terms of coverage probability and interval length for the confidence interval of variance from linear regression models. The proposed jackknife empirical likelihood method has better performance. We also illustrate the proposed methods using two real data sets.
DOI
https://doi.org/10.57709/4313705
Recommended Citation
Lin, Hui-Ling, "Jackknife Empirical Likelihood for the Variance in the Linear Regression Model." Thesis, Georgia State University, 2013.
doi: https://doi.org/10.57709/4313705