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Sharp convergence rates for forward regression in high-dimensional sparse linear models


Kozbur, Damian (2017). Sharp convergence rates for forward regression in high-dimensional sparse linear models. Working paper series / Department of Economics 253, University of Zurich.

Abstract

Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-dimensional sparse linear models. Probabilistic bounds for prediction error norm and number of selected covariates are proved. The analysis in this paper gives sharp rates and does not require β-min or irrepresentability conditions.

Abstract

Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-dimensional sparse linear models. Probabilistic bounds for prediction error norm and number of selected covariates are proved. The analysis in this paper gives sharp rates and does not require β-min or irrepresentability conditions.

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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Working Paper Series > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Forward regression, high-dimensional models, sparsity, model selection
Language:English
Date:May 2017
Deposited On:24 May 2017 12:36
Last Modified:09 Dec 2017 01:04
Series Name:Working paper series / Department of Economics
Number of Pages:18
ISSN:1664-7041
Official URL:http://www.econ.uzh.ch/static/wp/econwp253.pdf
Related URLs:http://www.econ.uzh.ch/static/workingpapers.php

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