empiricallikelihoodbasedsubsetselectionforpartiallylinearautoregressivemodels

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	empiricallikelihoodbasedsubsetselectionforpartiallylinearautoregressivemodels
	Han Yu 1; Jin Yinghua 2; Chen Min2
	2013
发表期刊	actamathematicaeapplicataesinica
ISSN	0168-9673
卷号	29 期号:4 页码:793
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.
语种	英语
资助项目	[National Natural Science Foundation of China]
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/45179
专题	应用数学研究所
作者单位	1.吉林大学 2.中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Han Yu,Jin Yinghua,Chen Min. empiricallikelihoodbasedsubsetselectionforpartiallylinearautoregressivemodels[J]. actamathematicaeapplicataesinica,2013,29(4):793.
APA	Han Yu,Jin Yinghua,&Chen Min.(2013).empiricallikelihoodbasedsubsetselectionforpartiallylinearautoregressivemodels.actamathematicaeapplicataesinica,29(4),793.
MLA	Han Yu,et al."empiricallikelihoodbasedsubsetselectionforpartiallylinearautoregressivemodels".actamathematicaeapplicataesinica 29.4(2013):793.