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Abstract Details

Activity Number: 33
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305363
Title: Boosting Discrete Choice Model with Applications to Mobile Computer Sales Data
Author(s): Jay Wang*+
Companies: Hewlett-Packard Labs
Address: Information Analytics Lab, Palo Alto CA 94304, CA, , United States
Keywords: Boosting ; varying-coefficient model ; Discrete Choice Model ; Partially linear model ; multinomial logit
Abstract:

Estimating the aggregated market demand is intrinsically important to manufacturers and retailers. Motivated by the need for a demand model to facilitate pricing optimization at Hewlett-Packard, we have developed a Boosted Discrete Choice Model that takes product brand, features, price and sales volume as the input. In the proposed approach, the utility of a product is specified semiparametrically, either by a varying-coefficient linear model or a partially linear model. We formulate the multinomial likelihood and apply gradient boosting to maximize the likelihood. Several attraction functions like the multinomial logit (MNL), linear and constant elasticity of substitution (CES) attraction functions are compared empirically and the implications of the model estimates on pricing are discussed.


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