Lasso regression in sparse linear model with φ-mixing errors View Full Text


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Article Info

DATE

2022-02-18

AUTHORS

Ling Peng, Yan Zhu, Wenxuan Zhong

ABSTRACT

This paper investigates the Lasso method for sparse linear models with exponential φ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi $$\end{document}-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. More... »

PAGES

1-26

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00184-022-00860-7

DOI

http://dx.doi.org/10.1007/s00184-022-00860-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1145698701


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