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Abstract Details

Activity Number: 61
Type: Topic Contributed
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #305104
Title: Kernel Dimension Reduction in Data-Rich Marketing Research
Author(s): Yuexiao Dong*+
Companies: Temple University
Address: 3140 McLaughlin Ct, Garnet Valley, PA, 19060, United States
Keywords: Kernel trick ; Nonlinear dimension reduction ; Sliced inverse regression
Abstract:

In today's data-rich environments, firms have access to massive amounts of information about actual and potential customers. The availability of such information has led to great academic interest in dimension reduction techniques that can aid in identifying significant variables that increase purchases made by existing customers and attract new customers. Historically, principal components regression, partial least squares regression, canonical correlation analysis, ridge regression and sliced inverse regression have been deployed. These methods enable researchers to build parametric or semiparametric models, and relate a focal response variable to a few factors (or indexes). The shortcoming of these approaches, however, is that they can only rely on linear combinations of the regressors. In this paper, we draw upon recent developments from the machine learning literature to introduce kernel dimension reduction techniques. By projecting the regressors to a high-dimensional feature space, we are able to identify factors that are nonlinear combinations of the input variables. Simulation study and application in a marketing context show promising results.


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