On Eliminating Inferior Regression Models.

Abstract

Consider a linear regression model with (p-1) predictor variables which is taken as the 'true' model. The goal is to select of all possible reduced models such that all inferior models (to be defined) are excluded with a guaranteed minimum probability. A procedure is proposed for which the exact evaluation of the probability of a correct decision is difficult; however, it is shown that the probability requirement can be met for sufficiently large sample size. Monte Carlo evaluation of the constant associated with the procedure and some ways to reduce the amount of computations involved in the implementation of the procedure are discussed. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1981
Accession Number
ADA101924

Entities

People

  • Deng Yuang Huang
  • S. Panchapakesan

Organizations

  • Purdue University

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  • Computer science
  • Mathematics

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  • Calculus or Mathematical Analysis
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