This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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683
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Type:
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Contributed
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Date/Time:
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Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and Marketing
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| Abstract - #307501 |
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Title:
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A Hierarchical Bayesian Model for the Analysis of Customer Choice Data
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Author(s):
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Zhe Chen*+ and Duncan K.H. Fong
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Companies:
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Penn State and Penn State
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Address:
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325 Thomas Building, University Park, PA, 16802,
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Keywords:
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Bayesian method ;
Multinomial probit model ;
correlation matrix
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Abstract:
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Bayesian multinomial probit models are commonly used to analyze choice data in marketing. Because of identification issues, there is a controversy on the use of covariance matrix versus correlation matrix. Here we provide new insights in favor of using the correlation matrix when only limited choice data are available. We also provide an efficient algorithm to perform the required Bayesian computation. In studying this problem, we have found some mistakes in the literature and offered their corrections in this paper. Synthetic and real data sets are analyzed to illustrate the methodology.
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