Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches

  title={Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches},
  author={John Haslett and Andrew C. Parnell and John P. Hinde and Rafael Andrade Moral},
  journal={International Statistical Review},
  pages={216 - 236}
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… 

Generally-Altered, -Inflated, -Truncated and -Deflated Regression, With Application to Heaped and Seeped Data

Models such as the zero-inflated and zero-altered Poisson and zero-truncated binomial are well-established in modern regression analysis. We propose a super model that jointly and maximally unifies



Models for count data with many zeros

There has been considerable interest in models for count data that allow for excess zeros, particularly in the econometric literature, and these models complement more conventional models for overdispersion that concentrate on modelling the variance-mean relationship correctly.

Marginal zero-inflated regression models for count data

ABSTRACT Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate

Random effect models for repeated measures of zero-inflated count data

For count responses, the situation of excess zeros (relative to what standard models allow) often occurs in biomedical and sociological applications. Modeling repeated measures of zero-inflated count

Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study

This paper adapts Lambert's methodology to an upper bounded count situation, thereby obtaining a zero-inflated binomial (ZIB) model, and adds to the flexibility of these fixed effects models by incorporating random effects so that, e.g., the within-subject correlation and between-subject heterogeneity typical of repeated measures data can be accommodated.

Regression Models for Count Data in R

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in

What does a zero mean? Understanding false, random and structural zeros in ecology

Zeros (i.e. events that do not happen) are the source of two common phenomena in count data: overdispersion and zero‐inflation. Zeros have multiple origins in a dataset: false zeros occur due to

A marginalized zero‐inflated Poisson regression model with overall exposure effects

A marginalized ZIP model approach for independent responses to model the population mean count directly is developed, allowing straightforward inference for overall exposure effects and empirical robust variance estimation for overall log-incidence density ratios.

The Gamma-count distribution in the analysis of experimental underdispersed data

Event counts are response variables with non-negative integer values representing the number of times that an event occurs within a fixed domain such as a time interval, a geographical area or a cell

7 Count Response Regression Models