Empirical Likelihood-Based Subset Selection for Partially Linear Autoregressive Models

被引:0
作者
Yu HAN [1 ,2 ]
Ying-hua JIN [3 ]
Min CHEN [3 ]
机构
[1] College of Mathematics,Jilin University
[2] College of Science,Northeast Dianli University
[3] Academy of Mathematics and Systems Science,Chinese Academy of Sciences
基金
中国国家自然科学基金;
关键词
subset selection; empirical likelihood; partial linear autoregressive model;
D O I
暂无
中图分类号
O212.1 [一般数理统计];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Based on the empirical likelihood method, the subset selection and hypothesis test for parameters in a partially linear autoregressive model are investigated. We show that the empirical log-likelihood ratio at the true parameters converges to the standard chi-square distribution. We then present the definitions of the empirical likelihood-based Bayes information criteria(EBIC) and Akaike information criteria(EAIC).The results show that EBIC is consistent at selecting subset variables while EAIC is not. Simulation studies demonstrate that the proposed empirical likelihood confidence regions have better coverage probabilities than the least square method, while EBIC has a higher chance to select the true model than EAIC.
引用
收藏
页码:793 / 808
页数:16
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