Empirical Likelihood-Based Subset Selection for Partially Linear Autoregressive Models  

作　　者：Yu HAN Ying-hua JIN Min CHEN 

机构地区：[1]College of Mathematics,Jilin University [2]College of Science,Northeast Dianli University [3]Academy of Mathematics and Systems Science,Chinese Academy of Sciences

出　　处：《Acta Mathematicae Applicatae Sinica》2013年第4期793-808,共16页应用数学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.10871188,10801123)

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.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.

关 键 词：subset selection empirical likelihood partial linear autoregressive model 

分 类 号：O212.1[理学—概率论与数理统计] S718.512[理学—数学]