The Joint Projected and Skew Normal

2015/12/01 by Gianluca Mastrantonio, Mastrantonio, Gianluca
Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Genetic and phenotypic traits in livestock #Wildlife Ecology and Conservation

paper · pdf · doi:10.48550/arxiv.1512.00487

openalex publication_date 2015/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce a new multivariate circular linear distribution suitable for modeling direction and speed in (multiple) animal movement data. To properly account for specific data features, such as heterogeneity and time dependence, a hidden Markov model is used. Parameters are estimated under a Bayesian framework and we provide computational details to implement the Markov chain Monte Carlo algorithm. The proposed model is applied to a dataset of six free-ranging Maremma Sheepdogs. Its predictive performance, as well as the interpretability of the results, are compared to those given by hidden Markov models built on all the combinations of von Mises (circular), wrapped Cauchy (circular), gamma (linear) and Weibull (linear) distributions

Citations

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