A Bayesian model for longitudinal circular data based on the projected normal distribution

Gabriel Nuñez-Antonio and Eduardo Gutiérrez-Peña

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 506-519

Abstract: The analysis of short longitudinal series of circular data may be problematic and to some extent has not been fully developed. A Bayesian analysis of a new model for such data is presented. The model is based on a radial projection onto the circle of a particular bivariate normal distribution. Inference about the parameters of the model is 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 using both simulated data sets and a real data set previously analyzed in the literature.

Keywords: Circular data; Gibbs sampler; Latent variables; Longitudinal data; Mixed-effects linear models; Projected normal distribution (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016794731200299X
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:506-519

DOI: 10.1016/j.csda.2012.07.025

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().