Abstract Details
Activity Number:
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403
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Marketing
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Abstract #311369
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Title:
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A Web-Based Marketing Data Analysis Application of Hierarchical Bayesian ANOVA
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Author(s):
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Chen Dong*+ and Michel Wedel
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Companies:
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University of Maryland and Robert H. Smith School of Business
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Keywords:
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Hierarchical Bayes ;
ANOVA ;
Markov chain Monte Carlo ;
BUGS modeling
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Abstract:
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A Web-based statistical application for hierarchical Bayesian ANOVA in marketing data analysis is presented in this paper. It offers an easy-to-use interface and advanced statistical routines for five Bayesian hierarchical regression models which are well suited for marketing research. Markov chain Monte Carlo (MCMC) simulation is carried out in this application to simulate posterior samples of each model specified by the user. It takes data, model information and simulation parameters as inputs through the World Wide Web(WWW). The core program in server side is written in R and JAGS. It communicates with the server-side scripting language to obtain data and provide simulation results. The main aim of the menu driven application is to oer freely accessible resources for hierarchical Bayesian ANOVA analysis which is not easy to implement and complicated to do data post-processing. The only requirement on the part of the user is a comma-separated values(CSV) file and a web browser such as Internet Explorer, Chrome and Firefox.
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Authors who are presenting talks have a * after their name.
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