Association Journal of CSIAM
Supervised by Ministry of Education of PRC
Sponsored by Xi'an Jiaotong University
ISSN 1005-3085  CN 61-1269/O1

Chinese Journal of Engineering Mathematics ›› 2019, Vol. 36 ›› Issue (5): 595-610.doi: 10.3969/j.issn.1005-3085.2019.05.009

Previous Articles    

Empirical Likelihood for Linear Models under Strongly Mixing Samples

CHEN Yu-qiu,  QIN Yong-song   

  1. Department of Statistics, Guangxi Normal University, Guilin 541004
  • Received:2017-04-25 Accepted:2019-06-06 Online:2019-10-15 Published:2019-12-15
  • Contact: Y. Qin. E-mail address: ysqin@mailbox.gxnu.edu.cn
  • Supported by:
    The National Natural Science Foundation of China (11671102); the Natural Science Foundation of Guangxi Province (2016GXNSFAA3800163; 2017GXNSFAA198349);  the Program on the High Level Innovation Team and Outstanding Scholars in Universities of Guangxi Province.

Abstract: Dependent data are popular in applications. The dependence described by strong mixing  is the weakest among well-known mixing structures, which appears in many application fields such as the pricing theories of financial assets. In this paper, by applying the blockwise empirical likelihood (EL) approach, the EL-based confidence regions for the regression vector in a linear model under strongly mixing errors are established, which can be used for the interval estimation and hypothesis testing of the regression vector. Results of a small simulation study on the finite sample performance of the confidence regions are provided.

Key words: strong mixing, linear model, blockwise empirical likelihood; confidence region

CLC Number: