Kent, JT orcid.org/0000-0002-1861-8349, Ganeiber, AM and Mardia, KV orcid.org/0000-0003-0090-6235 (2018) A New Unified Approach for the Simulation of a Wide Class of Directional Distributions. Journal of Computational and Graphical Statistics, 27 (2). pp. 291-301. ISSN 1061-8600
Abstract
The need for effective simulation methods for directional distributions has grown as they have become components in more sophisticated statistical models. A new acceptance-rejection method is proposed and investigated for the Bingham distribution on the sphere using the angular central Gaussian distribution as an envelope. It is shown that the proposed method has high efficiency and is also straightforward to use. Next, the simulation method is extended to the Fisher and Fisher-Bingham distributions on spheres and related manifolds. Together, these results provide a widely applicable and efficient methodology to simulate many of the standard models in directional data analysis. An R package simdd, available in the online supplementary material, implements these simulation methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. This is an author produced version of a paper published in Journal of Computational and Graphical Statistics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | acceptance-rejection; angular central Gaussian distribution; Bingham distribution; bivariate von Mises sine distribution; matrix Fisher distribution; simulation efficiency |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Jan 2019 11:32 |
Last Modified: | 26 Aug 2020 01:18 |
Status: | Published |
Publisher: | Taylor and Francis |
Identification Number: | 10.1080/10618600.2017.1390468 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123206 |