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A Bayesian model for longitudinal circular data

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  • Gabriel Núñez-Antonio

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  • Eduardo Gutiérrez-Peña

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    Abstract

    The 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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    File URL: http://e-archivo.uc3m.es/bitstream/10016/12076/1/ws112720.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws112720.

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    Date of creation: Sep 2011
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    Handle: RePEc:cte:wsrepe:ws112720

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    Related research

    Keywords: Circular data; Longitudinal data; Gibbs sampler; Latent variables; Mixed-effects linear models; Projected normal distribution;

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    1. 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.
    2. 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.
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