Bayesian variable selection for binary response models and direct marketing forecasting

Authors:
Geng Cui;Man Leung Wong;Guichang Zhang
Affiliations:
Department of Marketing and International Business, Lingnan University, Tuen Mun, N.T., Hong Kong;Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, N.T., Hong Kong;Department of Economics, Ocean University of China, Qingdao, Shandong 266071, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Selecting good variables to build forecasting models is a major challenge for direct marketing given the increasing amount and variety of data. This study adopts the Bayesian variable selection (BVS) using informative priors to select variables for binary response models and forecasting for direct marketing. The variable sets by forward selection and BVS are applied to logistic regression and Bayesian networks. The results of validation using a holdout dataset and the entire dataset suggest that BVS improves the performance of the logistic regression model over the forward selection and full variable sets while Bayesian networks achieve better results using BVS. Thus, Bayesian variable selection can help to select variables and build accurate models using innovative forecasting methods.