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Inference for circular distributions and processes.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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  • Stuart Coles
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<mark>Journal publication date</mark>1998
<mark>Journal</mark>Statistics and Computing
Issue number2
Volume8
Number of pages9
Pages (from-to)105-113
Publication StatusPublished
<mark>Original language</mark>English

Abstract

One approach to defining models for circular data and processes has been to take a standard Euclidean model and to wrap it around the circle. This generates rich families of circular models but creates difficulties for inference. Using data augmentation ideas which have previously been applied to this problem in the framework of an EM algorithm, we demonstrate the power and flexibility of Markov chain Monte Carlo methods to fit such classes of models to circular data. The precision of the technique is confirmed through simulated examples, and then applications are given to multivariate and time series data of wind directions.