Using mixed integer optimisation to select variables for a store choice model
by Toshiki Sato; Yuichi Takano; Takanobu Nakahara
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 5, No. 2, 2016

Abstract: This paper develops a store choice model to investigate consumers' store choice behaviour through the use of actual scanner panel data. For this purpose, we use the variable selection method proposed by Sato et al. (2015), which is based on mixed integer optimisation for logistic regression. Computational results demonstrated that when the Akaike information criterion is used as a goodness-of-fit measure, our approach yields a predictive performance better than that obtained using the common stepwise method of variable selection. Moreover, we clarified store choice factors by analysing the results of variable selection.

Online publication date: Wed, 20-Apr-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com