B1495
Title: Sparse predictive modeling for bank telemarketing success using smooth-threshold estimating equations
Authors: Yoshinori Kawasaki - The Institute of Statistical Mathematics (Japan) [presenting]
Masao Ueki - RIKEN Center for Advanced Intelligence Project (Japan)
Abstract: We attempt to build and evaluate several predictive models to predict success of telemarketing calls for selling bank long-term deposits using a publicly available data from a Portuguese retail bank collected from 2008 and 2013. 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 parameterization 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. In addition to variable selection, the smooth-threshold estimating equation can achieve automatic grouping of predictor variables, which is an alternative sparse modeling to variable selection and could be suited to a certain problem, e.g., dummy variables created from categorical predictor variable. Predictive power of each modeling is assessed by repeating cross-validation experiments or sample splitting, one for training and another for testing.
