Jackknife-blockwise empirical likelihood methods under dependence

Rongmao Zhang, Liang Peng and Yongcheng Qi

Journal of Multivariate Analysis, 2012, vol. 104, issue 1, 56-72

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: Confidence; region; Empirical; likelihood; General; estimating; equations; Jackknife; Weak; dependence (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (3)

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