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Activity Number: 30 - Bayesian Modeling and Time Series
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #323424
Title: Nonparametric Bayesian Survival Models in a Demand Forecasting Application
Author(s): Ta-Hsin Li*
Companies: IBM Watson Research Center
Keywords: survival analysis ; demand forecasting ; nonparametric ; Bayesian ; hierarchical

The problem of data fragmentation is encountered when incorporating certain categorical covariates in a demand forecasting application using survival models. A Bayesian approach is taken to deal with the problem, which enables data fusion across the data segments defined by the covariates. In particular, a nonparametric Bayesian estimator is employed to estimate the survival function of each segment, where the priors are specified by taking advantage of a data segment hierarchy combined with an empirical Bayes method. The resulting hierarchical nonparametric Bayesian models are compared with a class-based modeling approach for demand forecasting using a real data set from a resource-pool-based operation of software development.

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

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