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Activity Number: 445
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320105
Title: Sparse Predictive Modeling for Bank Telemarketing Success Using Smooth-Threshold Estimating Equations
Author(s): Yoshinori Kawasaki* and Masao Ueki
Companies: Institute of Statistical Mathematics and Kurume University
Keywords: Automatic grouping ; Bank telemarketing ; Smooth threshold estimating equation ; Variable selection

We build and evaluate several predictive models to predict success of telemarketing calls for selling bank long-term deposits using a publicly available set of data from a retail bank. The data include multiple predictor variables, either numeric or categorical, related with bank client, product and social-economic attributes. Dealing with a categorical predictor variable as multiple dummy variables increases model dimensionality, and redundancy in model parametrization must be of practical concern. This motivates us to assess prediction performance with more parsimonious modeling. We apply contemporary variable selection methods with penalization including lasso, elastic net, smoothly-clipped absolute deviation, minimum concave penalty as well as the smooth-threshold estimating equation (STEE). In addition to variable selection, the STEE can achieve automatic grouping of predictor variables, which is an alternative sparse modeling to perform variable selection. Predictive power of each modeling approach is assessed by repeating cross-validation experiments or sample splitting, one for training and another for testing.

Authors who are presenting talks have a * after their name.

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