A flexible Bayesian mixture approach for multi-modal circular data

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2022-01-01

Author

Kilic, Muhammet Burak
Kalaylıoğlu Akyıldız, Zeynep Işıl
SenGupta, Ashis

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In this article, we consider multi-modal circular data and nonparametric inference. We introduce a doubly flexible method based on Dirichlet process circular mixtures in which parameter assumptions are relaxed. We assess and discuss in simulation studies the effi-ciency of the proposed extension relative to the standard finite mixture applications in the analysis of multi-modal circular data. The real data application shows that this relaxed approach is promising for making important contributions to our understanding of many real-life phenomena particularly in environmental sciences such as animal orientations.

Subject Keywords

&nbsp, directional data, Dirichlet process prior, mixture models, stick breaking construction, animal orientation

URI

https://hdl.handle.net/11511/99619

Journal

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS

DOI

https://doi.org/10.15672/hujms.897144

Collections

Department of Statistics, Article

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Citation Formats

M. B. Kilic, Z. I. Kalaylıoğlu Akyıldız, and A. SenGupta, “A flexible Bayesian mixture approach for multi-modal circular data,” HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, vol. 51, no. 4, pp. 1160–1173, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/99619.