Predicting Magazine Audiences with a Loglinear Model.

Abstract

A loglinear model for predicting magazine exposure distributions is developed and its' parameters are estimated by using the maximum likelihood technique. The accuracy of the loglinear and a Dirichlet-multinomial model are compared using 1985 AGB: McNair data. The result show that the loglinear model has significantly smaller prediction errors than the Dirichlet-multinomial model. A simple algorithm for optimal media scheduling is given. Keywords: Advertising; Statistical analysis; Efficiency. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1987
Accession Number
ADA186043

Entities

People

  • Peter J. Danaher

Organizations

  • Florida State University

Tags

Communities of Interest

  • C4I
  • Cyber
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Analysis Of Variance
  • Binomials
  • Commerce
  • Computations
  • Computer Programming
  • Dynamic Programming
  • Integer Programming
  • Maximum Likelihood Estimation
  • New York
  • Operations Research
  • Probability
  • Statistical Analysis
  • Statistics
  • Surveys
  • Three Dimensional

Fields of Study

  • Mathematics

Readers

  • Naval Personnel Management
  • Nuclear and Radiation Engineering.
  • Operations Research