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Jackknife-blockwise empirical likelihood methods under dependence
Authors:Rongmao Zhang  Yongcheng Qi
Institution:
  • a Department of Mathematics, Zhejiang University, Hanzhou, Zhejiang, China
  • b School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0160, USA
  • c Department of Mathematics and Statistics, University of Minnesota Duluth, 1117 University Drive, Duluth, MN 55812, USA
  • 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.
    Keywords:primary  62M10  62E20  secondary  60F17
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