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Maximum Entropy Dirichlet Modeling of Categorical Data With Application to Consumer Choice

Publish place: 05th Iranian Statistics Conference
Publish Year: 1379
Type: Conference paper
Language: English
View: 1,521

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Link to this Paper:
https://civilica.com/doc/84772

Document National Code:

ISC05_022

Index date: 4 January 2010

Maximum Entropy Dirichlet Modeling of Categorical Data With Application to Consumer Choice abstract

We use the Maximum Entropy Dirichlet (MED) procedure to model consumer choice of long distance provider based on the perceived attributes of the companies. The MED is a computer-intensive method that uses Dirichlet prior and various attribute constraints as inputs and provides maximum entropy models that are in loglinear and logit forms. The MED generates prior and posterior distributions for the parameters of each model and for a Kullback-Leibler information function that measures the fit of the model. The MED also provides posterior distribution for inference about a normalized Kullback-Leibler information index of fit.

Maximum Entropy Dirichlet Modeling of Categorical Data With Application to Consumer Choice Keywords:

Contingency tables , Kullback-Leibler information , Logit , Logistic regression , Loglinear , Noninformative prior.

Maximum Entropy Dirichlet Modeling of Categorical Data With Application to Consumer Choice authors

Thomas A. Mazzuchi

School of Engineering and Applied Science, The George Washington University, Washington D.C. ۲۰۰۵۲

Ehsan S. Soofi

Scool of Business Administration, University of Wiscoonsin-Milwaukee, P.O. Box ۷۴۲, Milwaukee, WI ۵۳۲۰۱

Refilk Soyer

Department of Management Science, The George Washington University, Washington D.C. ۲۰۰۵۲

Joseph J. Retzer

Maritz Marketing Resear Inc., ۱۴۱۵ W. ۲۲nd Street, Suite ۸۰۰, Oak Brook, IL ۶۰۵۲۳, USA