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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

Mean Square Error Partial Little Square Bayesian Information Criterion Coefficient Estimate Ridge Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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