Some bivariate distributions on a discrete torus with application to wind direction datasets

TL;DR

This paper introduces two new bivariate discrete circular distributions, BWG and BGWG, tailored for wind direction data, providing analytical tractability and practical estimation methods.

Contribution

The paper develops novel bivariate wrapped geometric distributions for discrete circular data, with explicit formulas and application to wind direction datasets.

Findings

Models fit wind direction data effectively

Closed-form expressions facilitate parameter estimation

Application demonstrates practical utility

Abstract

Many datasets are observed on a finite set of equally spaced directions instead of the exact angles, such as the wind direction data. However, in the statistical literature, bivariate models are only available for continuous circular random variables. This article presents two bivariate circular distributions, namely bivariate wrapped geometric (BWG) and bivariate generalized wrapped geometric (BGWG), for analyzing bivariate discrete circular data. We consider wrapped geometric distributions and a trigonometric function to construct the models. The models are analytically tractable due to the exact closed-form expressions for the trigonometric moments. We thoroughly discuss the distributional properties of the models, including the interpretation of parameters and dependence structure. The estimation methodology based on maximizing the likelihood functions is illustrated for simulated datasets. Finally, the proposed distributions are utilized to analyze pairwise wind direction measurements obtained at different stations in India, and the interpretations for the fitted models are briefly discussed.