A Bayesian model for longitudinal circular data
AbstractThe analysis of short longitudinal series of circular data may be problematic and to some extent has not been completely developed. In this paper we present a Bayesian analysis of a model for such data. The model is based on a radial projection onto the circle of a particular bivariate normal distribution. Inferences about the parameters of the model are based on samples from the corresponding joint posterior density which are obtained using a Metropolis-within-Gibbs scheme after the introduction of suitable latent variables. The procedure is illustrated both using a simulated data set and a realdata set previously analyzed in the literature.
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Bibliographic InfoPaper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws112720.
Date of creation: Sep 2011
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Circular data; Longitudinal data; Gibbs sampler; Latent variables; Mixed-effects linear models; Projected normal distribution;
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- Rinaldo Artes, 2000. "Longitudinal Data Estimating Equations for Dispersion Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 27(2), pages 321-334.
- Gabriel Nunez-Antonio & Eduardo Gutierrez-Pena, 2005. "A Bayesian analysis of directional data using the projected normal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(10), pages 995-1001.
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