We exploit Gaussian copulas to specify a class of multivariate circular distributions and obtain parametric models for the analysis of correlated circular data. This approach provides a straightforward extension of traditional multivariate normal models to the circular setting, without imposing restrictions on the marginal data distribution nor requiring overwhelming routines for parameter estimation. The proposal is illustrated on two case studies of animal orientation and sea currents, where we propose an autoregressive model for circular time series and a geostatistical model for circular spatial series.

翻译：本文利用高斯Copula构建一类多元圆分布，为分析关联圆数据提供参数化模型。该方法将传统多元正态模型直接扩展至圆数据场景，既无需对边缘数据分布施加限制，也不要求复杂的参数估计流程。通过动物定向与洋流两个案例研究，我们分别提出了适用于圆时间序列的自回归模型和适用于圆空间序列的地统计学模型。