Lasso for sparse linear regression with exponentially beta-mixing errors

doi:10.1016/j.spl.2017.01.023

UM > Faculty of Science and Technology > DEPARTMENT OF MATHEMATICS

Residential College	false
Status	已發表Published
	Lasso for sparse linear regression with exponentially beta-mixing errors
	Xie, Fang 1; Xu, Lihu1 ; Yang, Youcai 2
	2017-01
Source Publication	STATISTICS & PROBABILITY LETTERS
ABS Journal Level	2
ISSN	0167-7152
Volume	125 Pages:64-70
Abstract	We prove two consistency theorems for the lasso estimators of sparse linear regression models with exponentially beta-mixing errors, in which the number of regressors p is large, even much larger than the sample size n.
Keyword	Lasso Linear Regression Models Consistency Exponentially Beta-mixing Errors
DOI	10.1016/j.spl.2017.01.023
URL	View the original
Indexed By	SCIE
Language	英語English
WOS Research Area	Mathematics
WOS Subject	Statistics & Probability
WOS ID	WOS:000399628000008
Publisher	ELSEVIER SCIENCE BV
The Source to Article	WOS
Scopus ID	2-s2.0-85012037042
Fulltext Access	View Full-Text via DOI View Full-Text via Web of Science
Citation statistics
Document Type	Journal article
Collection	DEPARTMENT OF MATHEMATICS
Corresponding Author	Xu, Lihu
Affiliation	1.Department of Mathematics, Faculty of Science and Technology, University of Macau, China 2.School of Economics and Management, Qingdao University of Science and Technology, China
First Author Affilication	Faculty of Science and Technology
Corresponding Author Affilication	Faculty of Science and Technology
Recommended Citation GB/T 7714	Xie, Fang,Xu, Lihu,Yang, Youcai. Lasso for sparse linear regression with exponentially beta-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 beta-mixing errors. STATISTICS & PROBABILITY LETTERS, 125, 64-70.
MLA	Xie, Fang,et al."Lasso for sparse linear regression with exponentially beta-mixing errors".STATISTICS & PROBABILITY LETTERS 125(2017):64-70.

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