Ridge Regression for Nonstandardized Models.

Abstract

In the usual regression model y = Xbeta + epsilon with X of full rank and epsilon approx. N(O, sigma squared I), the ordinary least squares estimator beta = (X'X) inverse X'y has extremely large mean square error when X is an ill-conditioned matrix. This paper compares ridge estimators for beta that arise when the biasing factor (k) is applied at different stages of standardization (i.e., centering and scaling), and shows which estimators are identical and which are different. In addition, results of a small-scale simulation are discussed.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1977
Accession Number
ADA045406

Entities

People

  • Dennis E. Smith
  • Terry L. King

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Classification
  • Data Science
  • Eigenvalues
  • Eigenvectors
  • Estimators
  • Information Science
  • Military Research
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  • Simulations
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Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Analytical Mechanics
  • Approximation Theory.