A flexible Bayesian mixture approach for multi-modal circular data

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

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.51, sa.4, ss.1160-1173, 2022 (SCI-Expanded, Scopus, TRDizin)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 51 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.15672/hujms.897144
  • Dergi Adı: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1160-1173
  • Anahtar Kelimeler: &nbsp, directional data, Dirichlet process prior, mixture models, stick breaking construction, animal orientation
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.