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)  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.
Bibliographical noteFunding Information:
We thank two reviewers and an associate editor for their helpful comments. Zhang’s research was supported by NSFC grant 10801118 , Peng’s research was supported by NSA grant H98230-10-1-0170 and NSF grant DMS-1005336 , and Qi’s research was supported by NSA grant H98230-10-1-0161 and NSF grant DMS-1005345 .
- Confidence region
- Empirical likelihood
- General estimating equations
- Weak dependence