A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model

Gul Inan (), John Preisser () and Kalyan Das ()
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Gul Inan: Middle East Technical University
John Preisser: University of North Carolina
Kalyan Das: University of Calcutta

Journal of Agricultural, Biological and Environmental Statistics, 2018, vol. 23, issue 1, No 7, 113-128

Abstract: Abstract Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151–5165, 2014) provide direct inference on overall exposure effects. Unlike standard zero-inflated models, marginalized models specify a regression model component for the marginal mean in addition to a component for the probability of an excess zero. This study proposes a score test for testing a marginalized zero-inflated Poisson model against a marginalized zero-inflated negative binomial model for model selection based on an assessment of over-dispersion. The sampling distribution and empirical power of the proposed score test are investigated via a Monte Carlo simulation study, and the procedure is illustrated with data from a horticultural experiment. Supplementary materials accompanying this paper appear on-line.

Keywords: Count data; Excess zeros; Marginal models; Over-dispersion; Score test (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s13253-017-0314-5

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