Bayesian inference for skew-wrapped Cauchy mixture model using a modified Gibbs sampler
Najmeh Nakhaei Rad, Andriëtte Bekker, Mohammad Arashi · 2021
More and more datasets on the circle tend to exhibit non-trivial features such as skewness and multimodality. Therefore, there is a growing demand for flexible models to capture these characteristics. For analyzing data with skew and multimodal patterns, we focus on the skew mixture models, specially skew-wrapped Cauchy mixture model as the underlying distribution. We incorporate prior knowledge about the parameters into analysis to improve results. A modified Gibbs sampling is used to obtain the estimates of the parameters. A simulation study is conducted to assess the performance of the proposed Bayesian approach for skew-wrapped Cauchy mixture model. A real dataset illustrates the usefulness of this approach.