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Abstract Details
Activity Number:
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586
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 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 - #301547 |
Title:
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Tree-Based Time Varying-Coefficient Regression For Product Demand Prediction
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Author(s):
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Jay Wang*+ and Kay-Yut Chen and Guillermo Gallego
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Companies:
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Hewlett Packard Laboratories and Hewlett Packard Laboratories and Columbia University
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Address:
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1501 Page Mill Rd, Palo Alto, CA, 94304,
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Keywords:
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elasticity ;
splines ;
tree-based methods ;
varying-coefficient regression
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Abstract:
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Estimating the aggregated market demand for a product in a dynamic market is intrinsically important to manufacturers and retailers. Motivated by the need for a statistical demand prediction model to facilitate laptop pricing at Hewlett-Packard, we have developed a novel "partitioned regression" model that is easily interpretable and have good prediction power. The proposed method is generally applicable to situations where predictions are made based on a large number of categorical variables and a much smaller number of continuous variables. Similar to regression trees, the partitioned regression uses a "greedy algorithm" to find optimal partitions of the products, where "optimality" refers to the maximal reduction of $-2\log$ likelihood. Within each partition (or "market segment"), a time varying-coefficient regression model is built to incorporate the temporal dynamics of the demand-price relationship, to reflect the change of market volume and customer preference at different phases of product life cycle. The proposed methodology is applied to a real dataset containing laptop sales of all brands covering September 2009 to June 2010 in all five states of Australia.
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