Inferences based on regression models for a directional response are usually problematic. This paper presents a Bayesian analysis of a regression model for circular data using the projected normal distribution. Inferences about the model are based on samples from the posterior densities which are obtained using the Gibbs sampler after the introduction of suitable latent variables. The problem of missing data in the response variable is also addressed in this context as is the use of a predictive criterion for model selection. The procedures are illustrated using two simulated datasets a dataset previously analysed in the literature and a real dataset concerning wind directions.
This paper presents a Bayesian analysis of the projected normal distribution, which is a flexible and useful distribution for the analysis of directional data. We obtain samples from the posterior distribution using the Gibbs sampler after the introduction of suitably chosen latent variables. The procedure is illustrated using simulated data as well as a real data set previously analysed in the literature.Circular data, Gibbs sampler, latent variables, radial projection, spherical data,
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