A note on the two-sample mean problem based on jackknife empirical likelihood |
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| Authors: | Xinqi Wu Sanguo Zhang |
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| Institution: | 1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China;2. Key Laboratory of Big Data Mining and Knowledge Management of CAS, Beijing, China;3. Hubei Collaborative Innovation Center for Early Warning and Emergency Response Technology, Wuhan, China |
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| Abstract: | In this article, we employ the jackknife empirical likelihood (JEL) method to construct the confidence regions for the difference of the means of two d-dimensional samples. Compared with traditional EL for the two-sample mean problem, JEL is extremely simpler to use in practice and is more effective in computing. 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. The effectiveness of the proposed method is demonstrated by a simulation study and a real data analysis. |
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| Keywords: | Bartlett correction coverage accuracy jackknife empirical likelihood two-sample problem |
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