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Prediction of U.S. Cancer Mortality Counts Using Semiparametric Bayesian Techniques

Prediction of U.S. Cancer Mortality Counts Using Semiparametric Bayesian Techniques We present two models for the short-term prediction of the number of deaths arising 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 any 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. This extension of the local linear model includes an additional “acceleration” term that allows it to quickly adjust to sudden changes in the time series. The proposed models can be used to obtain the predictive distributions of the future number of deaths, as well their means and variances through Markov chain Monte Carlo techniques. We illustrate our methods by runs on data from selected cancer sites. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Statistical Association Taylor & Francis

Prediction of U.S. Cancer Mortality Counts Using Semiparametric Bayesian Techniques

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References (21)

Publisher
Taylor & Francis
Copyright
© American Statistical Association
ISSN
1537-274X
eISSN
0162-1459
DOI
10.1198/016214506000000762
Publisher site
See Article on Publisher Site

Abstract

We present two models for the short-term prediction of the number of deaths arising 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 any 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. This extension of the local linear model includes an additional “acceleration” term that allows it to quickly adjust to sudden changes in the time series. The proposed models can be used to obtain the predictive distributions of the future number of deaths, as well their means and variances through Markov chain Monte Carlo techniques. We illustrate our methods by runs on data from selected cancer sites.

Journal

Journal of the American Statistical AssociationTaylor & Francis

Published: Mar 1, 2007

Keywords: Dirichlet process; Health statistics; Local linear model; Local quadratic model; Markov chain Monte Carlo; State-space model; Time series

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