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Lasso for sparse linear regression with exponentially β-mixing errors
Xie,Fang1; Xu,Lihu1; Yang,Youcai2
2017-02-15
Source PublicationSTATISTICS & PROBABILITY LETTERS
ABS Journal Level2
ISSN01677152
Volume125Pages:64-70
Abstract

We prove two consistency theorems for the lasso estimators of sparse linear regression models with exponentially β-mixing errors, in which the number of regressors p is large, even much larger than the sample size n.

KeywordConsistency Exponentially Β-mixing Errors Lasso Linear Regression Models
DOI10.1016/j.spl.2017.01.023
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000399628000008
Scopus ID2-s2.0-85012037042
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Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorXu,Lihu
Affiliation1.Department of MathematicsFaculty of Science and TechnologyUniversity of Macau,China
2.School of Economics and ManagementQingdao University of Science and Technology,China
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Xie,Fang,Xu,Lihu,Yang,Youcai. Lasso for sparse linear regression with exponentially β-mixing errors[J]. STATISTICS & PROBABILITY LETTERS, 2017, 125, 64-70.
APA Xie,Fang., Xu,Lihu., & Yang,Youcai (2017). Lasso for sparse linear regression with exponentially β-mixing errors. STATISTICS & PROBABILITY LETTERS, 125, 64-70.
MLA Xie,Fang,et al."Lasso for sparse linear regression with exponentially β-mixing errors".STATISTICS & PROBABILITY LETTERS 125(2017):64-70.
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