Dr. Jay Wang, Research Scientist, Information Analytics Lab (HP Labs)
Monday, April 30, 2012, 4:00 PM to 5:00 PM
Location: Engineering 2, Room 180
Hosted By Assistant Professor Qi Gong

Abstract:

Estimating the aggregated market demand for a product in a dynamic
market is intrinsically important to manufacturers and retailers. Motivated by the need for a statistical demand prediction model to facilitate laptop pricing at Hewlett-Packard, we have developed a novel boosting-based varying-coefficient regression model. The developed model uses regression trees as the base learner. Our method is generally applicable to varying-coefficient models with a large number of mixed-type varying-coefficient variables, which proves to be challenging for conventional nonparametric smoothing methods. The proposed approach works well in both predicting the response and estimating the varying-coefficient functions, based on a simulation study. Finally, we have applied this methodology to real-world mobile computer sales data for product demand prediction. This is joint work with Professor Trevor Hastie at Stanford University.

Biography:

Jay is a Research Scientist in the Information Analytics Lab (HP Labs), conducting cross-disciplinary research focusing on the interplay between statistics, economics, marketing and operations research. He received his PhD and MS in Statistics from Iowa State University and his BS in Management Science from the University of Science and Technology of China (USTC). Before joining HP, he worked as a Visiting Assistant Professor in Colorado State University and postdoctoral fellow in the National Institute of Statistical Sciences (NISS). Jay's research interest includes Interplay between statistics, economics, marketing and operations research; survey sampling, nonparametric statistics, and MCMC.