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
Publisher
Pergamon Press
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
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
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