A flexible Bayesian mixture approach for multi-modal circular data

Creative Commons License

Kilic M. B., KALAYLIOĞLU AKYILDIZ Z. I., SenGupta A.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, vol.51, no.4, pp.1160-1173, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.15672/hujms.897144
  • Journal Name: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1160-1173
  • Keywords: &nbsp, directional data, Dirichlet process prior, mixture models, stick breaking construction, animal orientation
  • Middle East Technical University Affiliated: Yes

Abstract

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.