Copula-like inference for discrete bivariate distributions with rectangular supports

TL;DR

This paper explores a copula-like approach for modeling discrete bivariate distributions with rectangular supports, using $I$-projections and iterative proportional fitting to decompose distributions and test goodness-of-fit.

Contribution

It introduces conditions for decomposing bivariate pmfs via $I$-projections, and develops estimation and goodness-of-fit methods for discrete copulas with theoretical and empirical validation.

Findings

Conditions for distribution decomposition established

Nonparametric and parametric estimation procedures developed

Goodness-of-fit tests with asymptotic properties analyzed

Abstract

After reviewing a large body of literature on the modeling of bivariate discrete distributions with finite support, \cite{Gee20} made a compelling case for the use of $I$-projections in the sense of \cite{Csi75} as a sound way to attempt to decompose a bivariate probability mass function (p.m.f.) into its two univariate margins and a bivariate p.m.f.\ with uniform margins playing the role of a discrete copula. From a practical perspective, the necessary $I$-projections on Fr\'echet classes can be carried out using the iterative proportional fitting procedure (IPFP), also known as Sinkhorn's algorithm or matrix scaling in the literature. After providing conditions under which a bivariate p.m.f.\ can be decomposed in the aforementioned sense, we investigate, for starting bivariate p.m.f.s with rectangular supports, nonparametric and parametric estimation procedures as well as goodness-of-fit tests for the underlying discrete copula. Related asymptotic results are provided and build upon a differentiability result for $I$-projections on Fr\'echet classes which can be of independent interest. Theoretical results are complemented by finite-sample experiments and a data example.