Modelling excess zeros in count data: A new perspective on modelling approaches

TL;DR

This paper provides a conceptual comparison of over-dispersion and zero-inflation models for count data with excess zeros, introduces a unifying framework for zero-inflation models, and proposes a new ZI model.

Contribution

It offers a novel theoretical framework for comparing ZI models and introduces a simpler, unified ZI model, enhancing understanding of modeling excess zeros.

Findings

Contrasts the impact of OD and ZI approaches on zeros.

Provides a unifying framework for ZI models, including hurdle and mixture models.

Proposes a new, simpler ZI model.

Abstract

We consider the analysis of count data in which the observed frequency of zero counts is unusually large, typically with respect to the Poisson distribution. We focus on two alternative modelling approaches: Over-Dispersion (OD) models, and Zero-Inflation (ZI) models, both of which can be seen as generalisations of the Poisson distribution; we refer to these as Implicit and Explicit ZI models, respectively. Although sometimes seen as competing approaches, they can be complementary; OD is a consequence of ZI modelling, and ZI is a by-product of OD modelling. The central objective in such analyses is often concerned with inference on the effect of covariates on the mean, in light of the apparent excess of zeros in the counts. Typically the modelling of the excess zeros per se is a secondary objective and there are choices to be made between, and within, the OD and ZI approaches. The contribution of this paper is primarily conceptual. We contrast, descriptively, the impact on zeros of the two approaches. We further offer a novel descriptive characterisation of alternative ZI models, including the classic hurdle and mixture models, by providing a unifying theoretical framework for their comparison. This in turn leads to a novel and technically simpler ZI model. We develop the underlying theory for univariate counts and touch on its implication for multivariate count data.