Lasso for sparse linear regression with exponentially β-mixing errors

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

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Bibliographic Details
Published in:Statistics & probability letters 2017-06, Vol.125, p.64-70
Main Authors: Xie, Fang, Xu, Lihu, Yang, Youcai
Format: Article
Language:English
Subjects:
Consistency
Exponentially [formula omitted]-mixing errors
Lasso
Linear regression models
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Description
Summary:We prove two consistency theorems for the lasso estimators of sparse linear regression models with exponentiallyβ-mixing errors, in which the number of regressors p is large, even much larger than the sample size n.
ISSN:0167-7152
1879-2103
DOI:10.1016/j.spl.2017.01.023