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

Date

2018-03-01

Author

İnan, Gül
Preisser, John
Das, Kalyan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Count data, Excess zeros, Marginal models, Over-dispersion, Score test

URI

https://hdl.handle.net/11511/57628

Journal

JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS

DOI

https://doi.org/10.1007/s13253-017-0314-5

Collections

Department of Statistics, Article

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Citation Formats

G. İnan, J. Preisser, and K. Das, “A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model,” JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, pp. 113–128, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57628.