Prediction in abundant high-dimensional linear regression

R. Dennis Cook, Liliana Forzani, Adam J. Rothman

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)3059-3088
Number of pages30
JournalElectronic Journal of Statistics
Volume7
Issue number1
DOIs
StatePublished - 2013

Keywords

  • Inverse regression
  • Least squares
  • Moore-Penrose inverse
  • Sparse covariance estimation

Fingerprint

Dive into the research topics of 'Prediction in abundant high-dimensional linear regression'. Together they form a unique fingerprint.

Cite this