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Activity Number:
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496
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #305351 |
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Title:
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Short-Term Prediction of Time Series Using Semiparametric Bayesian Techniques
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Author(s):
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Kaushik Ghosh*+ and Ram Tiwari
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Companies:
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New Jersey Institute of Technology and National Institutes of Health
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Address:
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Department of Mathematical Sciences, Room 606 Cullimore Hall, Newark, NJ, 07102,
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Keywords:
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Abstract:
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We present Bayesian local-linear and local-quadratic models for short-term prediction of time series. Dirichlet process priors are assumed in the distribution of the slope and acceleration terms, respectively, to make the model flexible and to accommodate various shapes of the series. Markov chain Monte Carlo techniques are used to obtain predicted values along with prediction intervals. We illustrate the proposed models using mortality data on common cancers in the United States and discuss how one would choose prior parameters to balance model smoothness with flexibility.
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