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- Title
A Log-Linear Model for Predicting Magazine Audiences.
- Authors
Danaher, Peter J.
- Abstract
A log-linear model for predicting magazine exposure distributions is developed and its parameters are estimated by the maximum likelihood technique. The log-linear model is compared empirically with the best-found model for equal-insertion schedules, one of Leckenby and Kishi's Dirichlet multinomial models. For unequal-insertion schedules the log-linear model is compared with the popular Metheringham beta-binomial model. The results show that the log-linear model has significantly smaller prediction errors than either of the other models.
- Subjects
LOG-linear models; PREDICTION models; MARKET penetration; PERIODICAL circulation; MARKETING of periodicals; MATHEMATICAL models of marketing; MARKETING research; ESTIMATION theory; MATHEMATICAL models; ADVERTISING campaigns
- Publication
Journal of Marketing Research (JMR), 1988, Vol 25, Issue 4, p356
- ISSN
0022-2437
- Publication type
Article
- DOI
10.2307/3172946