Each week, we select a recently published Open Access article to feature. This week’s article comes from International Statistical Review and proposes a multivariate Poisson model based on comonotonic shocks.

The article’s abstract is given below, with the full article available to read here.

Schulz, J., Genest, C., and Mesfioui, M. (2020) A multivariate Poisson model based on comonotonic shocks. International Statistical Review, https://doi.org/10.1111/insr.12408.

Multivariate count data arise naturally in practice. In analysing such data, it is critical to define a model that can accurately capture the underlying dependence structure between the counts. To this end, this paper develops a multivariate model wherein correlated Poisson margins are generated by a comonotonic shock vector. The proposed model allows for greater flexibility in the dependence structure than that of the classical construction, which relies on the convolution of vectors of common Poisson shock variables. Several probabilistic properties of the multivariate comonotonic shock Poisson model are established, and various estimation strategies are discussed in detail. The model is further studied through simulations, and its utility is highlighted using a real data application.