STATISTICS COLLOQUIUM
Xinwei Deng, Associate Professor
Department of Statistics
Virginia Tech
Convex Clustering for Generalized Linear Models with Applications to Purchase Likelihood Prediction
Abstract
In the IT service pricing, it is essential to have accurate prediction of the purchase likelihood of potential clients. However, the heterogeneity related to both clients and products results in very different purchase behaviors. It is not appropriate to use one global model on all data. There is a great need of constructing distinctive models under different data segments. Towards this aim, we propose a convex clustering approach to performing data segmentation and model fitting simultaneously. The proposed method ensures data points with a common model structure are grouped into the same segment. An efficient algorithm with desirable asymptotic properties is developed for parameter estimation. The performance of the proposed approach and its merits are illustrated by numerical examples and a case study with business data from a major IT service provider.
This is a joint work with several researchers at the T.J. IBM Watson Research Center.
DATE: Wednesday, September 12, 2018
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu