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doi:10.3969/j.issn.1001-4268.2018.05.007

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Abstract

In this paper, we apply the empirical likelihood technique to propose a new class of M-estimators and quantile estimators in the presence of some auxiliary information under strong mixing samples. It is shown that the proposed M-estimators and quantile estimators are consistent and asymptotically normally distributed with smaller asymptotic variances than those of the usual M-estimators and quantile estimators.

Key words�� strong mixing sample empirical likelihood;M-estimation quantile

PACS:

O212.7

Fund:

The project was partially supported by the National Natural Science Foundation of China (Grant No. 11671102), the Natural Science Foundation of Guangxi (Grant Nos.~2017GXNSFAA198349; 2016GXNSFAA3800163), the Program on the High Level Innovation Team and Outstanding Scholars in Universities of Guangxi Province and the Program on the Basic Ability Promotion for Young and Middle-aged Teachers of Guangxi.

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	LI Yinghua
	LI Yuan
	QIN Yongsong

Cite this article:

LI Yinghua,LI Yuan,QIN Yongsong. M-estimation and Quantile Estimation in the Presence of Auxiliary Information under Strong Mixing Sample[J]. CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST, 2018, 34(5): 515-532.

URL:

http://aps.ecnu.edu.cn/EN/10.3969/j.issn.1001-4268.2018.05.007 OR http://aps.ecnu.edu.cn/EN/Y2018/V34/I5/515