Detecting Difference Between Coefficients in Linear Model Using Jackknife Empirical Likelihood

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• 作者：Xinqi Wu ; Qingzhao Zhang ; Sanguo Zhang
• 关键词：Bartlett correction ; coverage accuracy ; Jackknife empirical likelihood ; linear regression model
• 刊名：Journal of Systems Science and Complexity
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：29
• 期：2
• 页码：542-556
• 全文大小：281 KB
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• 作者单位：Xinqi Wu (1) (2)
  Qingzhao Zhang (1) (2)
  Sanguo Zhang (1) (2)
  
  1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
  2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100049, China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Systems Theory and Control
  Applied Mathematics and Computational Methods of Engineering
  Operations Research/Decision Theory
  Probability Theory and Stochastic Processes
  
• 出版者：Academy of Mathematics and Systems Science, Chinese Academy of Sciences, co-published with Springer
• ISSN：1559-7067

文摘

Empirical likelihood has been found very useful in many different occasions. It usually runs into serious computational difficulties while jackknife empirical likelihood (JEL) is shown to be effective when applied to some complicated statistics. In this paper, to test the difference between coefficients of two linear regression models, the authors apply JEL to construct the confidence regions. Based on the JEL ratio test, a version of Wilks’ theorem is developed. Furthermore, to improve the coverage accuracy of confidence regions, a Bartlett correction is applied. Simulation studies are carried out to show the effectiveness of the proposed method in aspects of coverage accuracy. A real data set is analyzed with the proposed method as an example.