Jackknife-blockwise empirical likelihood methods under dependence |
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Authors: | Rongmao Zhang Yongcheng Qi |
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Institution: | a Department of Mathematics, Zhejiang University, Hanzhou, Zhejiang, Chinab School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0160, USAc Department of Mathematics and Statistics, University of Minnesota Duluth, 1117 University Drive, Duluth, MN 55812, USA |
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Abstract: | Empirical likelihood for general estimating equations is a method for testing hypothesis or constructing confidence regions on parameters of interest. If the number of parameters of interest is smaller than that of estimating equations, a profile empirical likelihood has to be employed. In case of dependent data, a profile blockwise empirical likelihood method can be used. However, if too many nuisance parameters are involved, a computational difficulty in optimizing the profile empirical likelihood arises. Recently, Li et al. (2011) 9] proposed a jackknife empirical likelihood method to reduce the computation in the profile empirical likelihood methods for independent data. In this paper, we propose a jackknife-blockwise empirical likelihood method to overcome the computational burden in the profile blockwise empirical likelihood method for weakly dependent data. |
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Keywords: | primary 62M10 62E20 secondary 60F17 |
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