Performance of Empirical Risk Minimization for Linear Regression with Dependent Data

Fuente: arXiv
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Autores principales: Brownlees, Christian, Guðmundsson, Guðmundur Stefán
Formato: Preprint
Publicado: 2021
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author Brownlees, Christian
Guðmundsson, Guðmundur Stefán
author_facet Brownlees, Christian
Guðmundsson, Guðmundur Stefán
contents This paper establishes bounds on the performance of empirical risk minimization for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors is not specified. The main results of this paper show that the empirical risk minimizer achieves the optimal performance (up to a logarithmic factor) in a dependent data setting.
format Preprint
id arxiv_https___arxiv_org_abs_2104_12127
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Performance of Empirical Risk Minimization for Linear Regression with Dependent Data
Brownlees, Christian
Guðmundsson, Guðmundur Stefán
Econometrics
Methodology
This paper establishes bounds on the performance of empirical risk minimization for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors is not specified. The main results of this paper show that the empirical risk minimizer achieves the optimal performance (up to a logarithmic factor) in a dependent data setting.
title Performance of Empirical Risk Minimization for Linear Regression with Dependent Data
topic Econometrics
Methodology
url https://arxiv.org/abs/2104.12127