Statistical Model Comparison in Marketing Research

Christopher B. Barry and Albert R. Wildt
Additional contact information
Christopher B. Barry: University of Texas at Austin and University of Florida
Albert R. Wildt: University of Florida

Management Science, 1977, vol. 24, issue 4, 387-392

Abstract: Analysis for marketing decisions often involves the consideration of several alternative statistical models. As an intermediate step prior to making policy decisions, a single model is typically selected and used to guide subsequent decisions. In this paper, Bayesian model comparison methods are shown to lead to a predictive distribution for the decision problem without the intermediate step of model selection. This approach utilizes all available information bearing on the validity of the alternative models, as well as information concerning model parameters, in order to draw inferences regarding the criterion of interest relative to the decision to be made. The procedure is illustrated in the context of an advertising budget decision in which the functional form of sales response to advertising is unknown.

Date: 1977
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.24.4.387 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:24:y:1977:i:4:p:387-392

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().