Empirical likelihood for non-degenerate U-statistics
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作者:
Jing, Bing-Yi
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Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, Hong Kong, Peoples R ChinaHong Kong Univ Sci & Technol, Dept Math, Hong Kong, Hong Kong, Peoples R China
Jing, Bing-Yi
[1
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Yuan, Junqing
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Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, Hong Kong, Peoples R ChinaHong Kong Univ Sci & Technol, Dept Math, Hong Kong, Hong Kong, Peoples R China
Yuan, Junqing
[1
]
Zhou, Wang
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Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117543, SingaporeHong Kong Univ Sci & Technol, Dept Math, Hong Kong, Hong Kong, Peoples R China
Zhou, Wang
[2
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机构:
[1] Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, Hong Kong, Peoples R China
Standard empirical likelihood for U-statistics is too computationally expensive. To overcome this computational difficulty, we reformulate the non-degenerate U-statistics as a sample mean of some "pseudo" observations in this paper, and show that the empirical log-likelihood ratio has an asymptotic chi-squared distribution under the second moment condition. The method is extremely simple to use, and yet provide better coverage accuracy in general than other alternative methods from our simulation studies. (c) 2007 Elsevier B. V. All rights reserved.