Cross Validation of Selection of Variables in Multiple Regression.

Abstract

Techniques and criterion for selection of the 'best' subset of variables to be used in a regression model are reviewed. A model was developed using the Automatic Interaction Detection (AID) algorithm as a pre-screening device for locating those variables most important to the regression including interaction terms. Five previous models including the one developed by AID and one developed by Westinghouse on avionic characteristic data are used in cross validation experiments to determine the predictive power of these models on a new set of data points using the same set of variables. A cross validation R(2) value is discussed as a criterion for choosing between competing models. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1979
Accession Number
ADA080407

Entities

People

  • Joseph Richard Cafarella Jr

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms
  • C4I
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Bomber Aircraft
  • Cargo Aircraft
  • Communication Systems
  • Computer Programs
  • Control Panels
  • Databases
  • Information Science
  • Navigation
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Regression Analysis
  • Transport Aircraft

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Computational Fluid Dynamics (CFD)
  • Naval Personnel Management