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empiricallikelihoodbasedsubsetselectionforpartiallylinearautoregressivemodels
Han Yu1; Jin Yinghua2; Chen Min2
2013
Source Publicationactamathematicaeapplicataesinica
ISSN0168-9673
Volume29Issue:4Pages:793
AbstractBased 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.
Language英语
Funding Project[National Natural Science Foundation of China]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/45179
Collection应用数学研究所
Affiliation1.吉林大学
2.中国科学院数学与系统科学研究院
Recommended Citation
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.
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