Title

Bayesian variable selection for binary response models and direct marketing forecasting

Document Type

Journal article

Source Publication

Expert Systems With Applications

Publication Date

12-1-2010

Volume

37

Issue

12

First Page

7656

Last Page

7662

Keywords

Bayesian variable selection, binary response models, distribution of priors, direct marketing, forecasting models

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

DOI

10.1016/j.eswa.2010.04.077

Print ISSN

09574174

E-ISSN

18736793

Publisher Statement

Copyright © 2010 Elsevier Ltd

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Full-text Version

Publisher’s Version

Recommended Citation

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