JSM 2004 Online Program

Abstract #302212

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Activity Number:	386
Type:	Contributed
Date/Time:	Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #302212
Title:	Bayesian Regression for Circular Data
Author(s):	Barbara J. George*+ and Kaushik Ghosh
Companies:	Defense Manpower Data Center and George Washington University
Address:	1600 Wilson Blvd., 4th Floor, Arlington, VA, 22209,
Keywords:	directional data ; semiparametric Bayes ; MCMC ; Dirichlet process
Abstract:	Circular data are observations that are represented as points on a unit circle. Times of day and wind direction are two such examples. The proposed work uses a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a Dirichlet process prior with a bivariate normal baseline. Use of this semiparametric Bayesian approach gives added flexibility to the model relative to frequentist approaches. Markov chain Monte Carlo techniques are used to fit the proposed model. This technique is illustrated using simulated data.

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