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Activity Number: 621
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Statistics in Mental Health Research
Abstract - #305827
Title: Bayesian DP Time-Varying Circumplex Model
Author(s): Zhaowei Hua*+ and David Dunson and Sy-Miin Chow and Hongtu Zhu
Companies: Millennium Pharmaceuticals, Inc. and Duke University and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Address: 18 Hamilton Rd, Apt 102, Arlington, MA, 02474, United States
Keywords: circumplex correlation ; emotion dynamics ; Dirichlet process ; ordinal scales ; Bayesian

In the analysis of longitudinal circumplex data, it is of interest to explore the dynamic transition pattern of affective experience. The circumplex model of affect postulates that the underlying structure of affective experience can be characterized as an ordering of affective states on the circumference of a circle. This article proposes a semiparametric Bayesian DP time-varying circumplex model. A Dirichlet proces is used to capture the latent clustering structure and a discrete time-varying circumplex model addresses the time dependent transition structure and the self-transition feature. We develop an efficient MCMC algorithm using the exact block Gibbs sampler. The performance of our method is assessed by a simulation study. We apply our method to analyze a data set of emotion ordinal scales from a clinical study of the dynamics of emotion regulation.

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