标题:Empirical likelihood inference for estimating equation with missing data
作者:Wang XiuLi; Chen Fang; Lin Lu
作者机构:[Wang XiuLi] Shandong Normal Univ, Sch Math Sci, Jinan 250014, Peoples R China.; [Chen Fang] Sun Yat Sen Univ, Nanfang Coll, Dept Elect Commun & Sof 更多
通讯作者:Lin, L
通讯作者地址:[Lin, L]Shandong Univ, Sch Math, Jinan 250100, Peoples R China.
来源:SCIENCE CHINA-MATHEMATICS
出版年:2013
卷:56
期:6
页码:1233-1245
DOI:10.1007/s11425-012-4504-x
关键词:empirical likelihood; estimating equation; kernel regression; missing at; random
摘要:In this article, empirical likelihood inference for estimating equation with missing data is considered. Based on the weighted-corrected estimating function, an empirical log-likelihood ratio is proved to be a standard chi-square distribution asymptotically under some suitable conditions. This result is different from those derived before. So it is convenient to construct confidence regions for the parameters of interest. We also prove that our proposed maximum empirical likelihood estimator is asymptotically normal and attains the semiparametric efficiency bound of missing data. Some simulations indicate that the proposed method performs the best.
收录类别:SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878798620&doi=10.1007%2fs11425-012-4504-x&partnerID=40&md5=12bad897def0e676a54da54f8e8c3fc5
TOP