Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

We present two models for short-term prediction of the number of deaths that arise from common cancers in the United States. The first is a local linear model, in which the slope of the segment joining the number of deaths for two consecutive time periods is assumed to be random with a nonparametric distribution, which has a Dirichlet process prior. For slightly, longer prediction periods, we present a local quadratic model. The proposed methods are used to obtain the predictive distributions of the future number of deaths through Markov chain Monte Carlo techniques. We illustrate our methods by runs on data from selected cancer sites and provide guidelines on how to choose prior parameters that balance model flexibility with degree of smoothness in the prediction process.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.