Variable Selection in Logistic Regression.

Abstract

In many situations, we are interested in selection of important variables which are adequate for prediction under a logistic regression model. In this paper, some selection procedures based on the information theoretic criteria are proposed, and these procedures are proved to be strongly consistent. Keywords: Maximum likelihood estimation; Multivariate analysis; Asymptotic expansion.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1987
Accession Number
ADA186032

Entities

People

  • L. C. Zhao
  • Paruchuri R. Krishnaiah
  • Z. D. Bai

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Asymptotic Series
  • Consistency
  • Data Science
  • Discriminant Analysis
  • Discrimination
  • Governments
  • Information Science
  • Maximum Likelihood Estimation
  • Multivariate Analysis
  • North Carolina
  • Probability
  • Statistics
  • United States
  • United States Government
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.