标题： 联合投影和偏态正态分布 显示英文标题

标题： The Joint Projected and Skew Normal

摘要： 我们引入了一种新的多元圆形线性分布，适用于建模（多个）动物运动数据中的方向和速度。为了充分考虑特定数据特征（如异质性和时间依赖性），使用了隐马尔可夫模型。参数在贝叶斯框架下估计，并提供了计算细节以实现马尔可夫链蒙特卡洛算法。所提出的模型应用于六只自由放养的马雷马牧羊犬的数据集。其预测性能以及结果的可解释性与基于 von Mises（圆形）、wrapped Cauchy（圆形）、gamma（线性）和 Weibull（线性）分布的所有组合构建的隐马尔可夫模型的结果进行了比较。 显示英文摘要

摘要： 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