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