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Bayesian variable selection for binary response models and direct marketing forecasting
Expert Systems With Applications
  • Geng CUI, Lingnan University, Hong Kong
  • Man Leung WONG, Lingnan University, Hong Kong
  • Guichang ZHANG, University of China, Qingdao, China
Document Type
Journal article
Publication Date
Pergamon Press
  • Bayesian variable selection,
  • binary response models,
  • distribution of priors,
  • direct marketing,
  • forecasting models

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. (C) 2010 Elsevier Ltd. All rights reserved.

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Copyright © 2010 Elsevier Ltd

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Citation Information
Cui, G., Wong, M. L., & Zhang, G. (2010). Bayesian variable selection for binary response models and directmarketing forecasting. Expert Systems with Applications, 37(12), 7656-7662. doi: 10.1016/j.eswa.2010.04.077