Lasso regression in sparse linear model with $$\varphi $$ φ -mixing errors

Ling Peng (), Yan Zhu () and Wenxuan Zhong
Additional contact information
Ling Peng: Jiangxi University of Economics and Finance
Yan Zhu: University of Shanghai for Science and Technology
Wenxuan Zhong: Shanghai University of International Business and Economics

Metrika: International Journal for Theoretical and Applied Statistics, 2023, vol. 86, issue 1, No 1, 26 pages

Abstract: Abstract This paper investigates the Lasso method for sparse linear models with exponential $$\varphi $$ φ -mixing errors under a fixed design, where the number of covariates p is large, or even much larger than the sample size n. The non-asymptotic concentration inequalities for the estimation and prediction errors of the Lasso estimators are given when the errors follow the Gaussian distribution and the sub-exponential distribution, respectively. The prediction and variable selection performance of Lasso estimators are further illustrated through numerical simulations. Finally, the results of the empirical application show that the Index Tracking Fund based on the sparse selection of Lasso can closely track the trend of the target index, and thus provide some useful guidance for the investors.

Keywords: Lasso; $$\varphi $$ φ -mixing sequence; Consistency theorem; 62J07 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00184-022-00860-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:86:y:2023:i:1:d:10.1007_s00184-022-00860-7

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-022-00860-7

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().