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Abstract Details
Activity Number:
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578
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #306835 |
Title:
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Model Selection Strategies Applied to Customer Research Data
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Author(s):
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Jorge Alejandro*+
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Companies:
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Market Probe
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Address:
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2655 N Mayfair Rd, Milwaukee, WI, 53226, United States
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Keywords:
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Bayesian model selection ;
Information criterion ;
Customer research ;
Multi-collinearity ;
Multiple linear regression ;
Quantile regression
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Abstract:
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Customer research focuses on modeling attitudinal outcomes that ultimately managers can act upon in order to influence the bottom line. Not surprisingly, a key element in this process is to be able to formulate what factors contribute to form customers' overall evaluations. However, oftentimes survey instruments are packed with a large number of attributes that even though could arguably be used to describe the customer experience, also pose a challenge due to the interrelatedness between them. Model selection can be seen as an alternative to dealing with a large number of attributes, with particular interest in the presence of multi-collinearity. In this presentation, common linear and nonlinear modeling techniques are applied to customer survey data while utilizing different model selection strategies including information-based and Bayesian regularization approaches. The results are compared in terms of model performance and the potential impact on managerial recommendation.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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