Linear Model Selection and Regularization

  • Gareth James
  • Daniela Witten
  • Trevor Hastie
  • Robert Tibshirani
Chapter
Part of the Springer Texts in Statistics book series (STS, volume 103)

Abstract

In the regression setting, the standard linear model
$$\displaystyle{ Y =\beta _{0} +\beta _{1}X_{1} + \cdots +\beta _{p}X_{p}+\epsilon }$$
(6.1)
is commonly used to describe the relationship between a response Y and a set of variables \(X_{1},X_{2},\ldots,X_{p}\). We have seen in  Chapter 3 that one typically fits this model using least squares.

Keywords

Income Shrinkage Marketing Expense Lasso 

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gareth James
    • 1
  • Daniela Witten
    • 2
  • Trevor Hastie
    • 3
  • Robert Tibshirani
    • 3
  1. 1.Department of Information and Operations ManagementUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  3. 3.Department of StatisticsStanford UniversityStanfordUSA

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