Semiparametric Bayesian Techniques for Problems in Circular Data

Kaushik Ghosh, Rao Jammalamadaka and Ram Tiwari

Journal of Applied Statistics, 2003, vol. 30, issue 2, pages 145-161

Abstract: In this paper, we consider the problems of prediction and tests of hypotheses for directional data in a semiparametric Bayesian set-up. Observations are assumed to be independently drawn from the von Mises distribution and uncertainty in the location parameter is modelled by a Dirichlet process. For the prediction problem, we present a method to obtain the predictive density of a future observation, and, for the testing problem, we present a method of computing the Bayes factor by obtaining the posterior probabilities of the hypotheses under consideration. The semiparametric model is seen to be flexible and robust against prior misspecifications. While analytical expressions are intractable, the methods are easily implemented using the Gibbs sampler. We illustrate the methods with data from two real-life examples.

Date: 2003
References: Add references at CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/0266476022000023712 (text/html)
Access to full text is restricted to subscribers.

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: http://EconPapers.repec.org/RePEc:taf:japsta:v:30:y:2003:i:2:p:145-161

Ordering information: This journal article can be ordered from
http://www.tandf.co.uk/journals/subscription.asp

Access Statistics for this article

Journal of Applied Statistics is edited by Professor Gopal K. Kanji

More articles in Journal of Applied Statistics from Taylor and Francis Journals
Series data maintained by Michael McNulty ().