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Activity Number:
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17
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and Marketing
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| Abstract - #300822 |
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Title:
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A Hierarchical Bayesian Procedure for Two-Mode Cluster Analysis
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Author(s):
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Duncan K. Fong*+ and Wayne S. Desarbo and John C. Liechty and Kim Saxton
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Companies:
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Pennsylvania State University and Pennsylvania State University and Pennsylvania State University and Eli Lilly & Company
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Address:
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707G Business Admin. Bldg., University Park, PA, 16802-3007,
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Keywords:
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cluster analysis ; hierarchical Bayesian analysis ; finite mixture models ; consumer psychology
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Abstract:
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This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters. In this manner, interrelationships between both sets of entities (rows and columns) are easily ascertained. We describe the technical details of the proposed two-mode clustering methodology including its Bayesian mixture formulation and a Bayes factor heuristic for model selection. We present a modest Monte Carlo analysis to investigate the performance of the proposed Bayesian two-mode clustering procedure with respect to synthetically created data whose structure and parameters are known. Next, a consumer psychology application is provided examining physician pharmaceutical prescription behavior for various brands of prescription/ethical drugs in the mental health market. We conclude by discussing several fertile areas for future research.
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