Copula-based models for correlated circular data

TL;DR

This paper introduces Gaussian copula-based multivariate circular models that extend normal models to circular data, enabling flexible analysis of correlated circular variables without complex estimation routines.

Contribution

It presents a novel copula-based framework for modeling correlated circular data, including autoregressive and geostatistical models for circular time and spatial series.

Findings

Effective modeling of animal orientation data

Successful application to sea current data

Flexible extension of multivariate normal models

Abstract

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.