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

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2000
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Abstract
In a 1992 Technometrics paper, Lambert (1992, 34, 1-14) described zero-inflated Poisson (ZIP) regression, a class of models for count data with excess zeros. In a ZIP model, a count response variable is assumed to be distributed as a mixture of a Poisson(lambda) distribution and a distribution with point mass of one at zero, with mixing probability p. Both p and lambda are allowed to depend on covariates through canonical link generalized linear models. In this paper, we adapt Lambert's methodology to an upper bounded count situation, thereby obtaining a zero-inflated binomial (ZIB) model. In addition, we add 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. We motivate, develop, and illustrate the methods described here with an example from horticulture, where both upper bounded count (binomial-type) and unbounded count (Poisson-type) data with excess zeros were collected in a repeated measures designed experiment.
Reference Key
openalex_W2102810005 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Daniel B. Hall
Journal biometrics
Year 2000
DOI
10.1111/j.0006-341x.2000.01030.x
URL
https://doi.org/10.1111/j.0006-341x.2000.01030.x
Keywords Keywords not found

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