JSM 2005 Online Program

Abstract #304173

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Activity Number:	102
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #304173
Title:	Bayesian Inference on Multiresolutional State Space Model with a Climate Data Example
Author(s):	Yongku Kim*+ and Mark Berliner
Companies:	The Ohio State University and The Ohio State University
Address:	1958 Neil Avenue, Cockins Hall, Columbus, OH, 43210-1247, United States
Keywords:	non-stationary state space model ; multiresolutional model ; Bayesian inference ; Bayesian model selection ; global temperature data
Abstract:	State space models are standard frameworks for modeling and forecasting dynamic systems, and in conjunction with the Kalman filter, have been used in a range of applications such as biology, economics, engineering, and climatology. Nonstationary state space models are especially interesting because usual natural phenomena are typically nonGaussian and/or nonstationary. In this paper, I introduce a nonstationary state space model using hierarchical modeling, where the coefficients of the main variables also follow a state space model. However, each of these models is allowed to have different scale times. I apply Bayesian inference for such a multiresolutional state space model applied to climate temperature data. To find the optimal multiresolution scheme, Bayesian model selection issues and model uncertainty problems are discussed.

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