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Inference for circular distributions and processes.

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Inference for circular distributions and processes. / Coles, Stuart.
In: Statistics and Computing, Vol. 8, No. 2, 1998, p. 105-113.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Coles, S 1998, 'Inference for circular distributions and processes.', Statistics and Computing, vol. 8, no. 2, pp. 105-113. https://doi.org/10.1023/A:1008930032595

APA

Coles, S. (1998). Inference for circular distributions and processes. Statistics and Computing, 8(2), 105-113. https://doi.org/10.1023/A:1008930032595

Vancouver

Coles S. Inference for circular distributions and processes. Statistics and Computing. 1998;8(2):105-113. doi: 10.1023/A:1008930032595

Author

Coles, Stuart. / Inference for circular distributions and processes. In: Statistics and Computing. 1998 ; Vol. 8, No. 2. pp. 105-113.

Bibtex

@article{aa17c515da704939b5dbe01abeb84415,
title = "Inference for circular distributions and processes.",
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.",
keywords = "circle - directional data - Markov chain Monte Carlo - time series - wind directions - wrapped processes",
author = "Stuart Coles",
year = "1998",
doi = "10.1023/A:1008930032595",
language = "English",
volume = "8",
pages = "105--113",
journal = "Statistics and Computing",
publisher = "Springer Netherlands",
number = "2",

}

RIS

TY - JOUR

T1 - Inference for circular distributions and processes.

AU - Coles, Stuart

PY - 1998

Y1 - 1998

N2 - 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.

AB - 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.

KW - circle - directional data - Markov chain Monte Carlo - time series - wind directions - wrapped processes

U2 - 10.1023/A:1008930032595

DO - 10.1023/A:1008930032595

M3 - Journal article

VL - 8

SP - 105

EP - 113

JO - Statistics and Computing

JF - Statistics and Computing

IS - 2

ER -