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2013 Prediction in abundant high-dimensional linear regression
R. Dennis Cook, Liliana Forzani, Adam J. Rothman
Electron. J. Statist. 7: 3059-3088 (2013). DOI: 10.1214/13-EJS872

Abstract

An abundant regression is one in which most of the predictors contribute information about the response, which is contrary to the common notion of a sparse regression where few of the predictors are relevant. We discuss asymptotic characteristics of methodology for prediction in abundant linear regressions as the sample size and number of predictors increase in various alignments. We show that some of the estimators can perform well for the purpose of prediction in abundant high-dimensional regressions.

Citation

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R. Dennis Cook. Liliana Forzani. Adam J. Rothman. "Prediction in abundant high-dimensional linear regression." Electron. J. Statist. 7 3059 - 3088, 2013. https://doi.org/10.1214/13-EJS872

Information

Published: 2013
First available in Project Euclid: 16 December 2013

zbMATH: 1279.62140
MathSciNet: MR3151762
Digital Object Identifier: 10.1214/13-EJS872

Subjects:
Primary: 62J05
Secondary: 62H12

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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