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Activity Number:
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72
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #303299 |
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Title:
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RE-EM Trees: A New Data Mining Approach to Longitudinal Data
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Author(s):
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Rebecca J. Sela*+ and Jeffrey S. Simonoff
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Companies:
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New York University and New York University
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Address:
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Stern School of Business, 44 West Fourth Street, New York, NY, 10012,
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Keywords:
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data mining ; longitudinal data ; regression trees ; panel data
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Abstract:
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This paper presents a methodology that combines the flexibility of tree-based estimation methods with the structure of random effects models for longitudinal data. We apply the resulting model and estimation method, called the RE-EM tree, to pricing in online transactions. We also perform extensive Monte Carlo experiments to show that our estimator improves in-sample and out-of-sample performance relative to regression trees without random effects and is comparable or superior to using linear models with random effects.
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