A score test for zero‐inflation in correlated count data
Copyright policy )A score test for zero‐inflation in correlated count data
AbstractTo account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero‐inflated Poisson mixed regression models is applicable for accommodating the within‐cluster dependence. In this paper, a score test for zero‐inflation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is further illustrated using a data set on recurrent urinary tract infections. Copyright © 2005 John Wiley & Sons, Ltd.
- University of Queensland Australia
- Curtin University Australia
- University of Queensland Australia
- City University of Hong Kong China (People's Republic of)
- University of Queensland Australia
Poisson Regression, Statistics & Probability, Score Test, 310, Models, Biological, Environmental & Occupational Health, 230203 Statistical Theory, Cohort Studies, 780101 Mathematical sciences, C1, Models, Risk Factors, Homes for the Aged, Humans, Computer Simulation, Poisson Distribution, Aged, Retrospective Studies, Models, Statistical, 230204 Applied Statistics, Research & Experimental, 0104 Statistics, Count Data, Public, Middle Aged, Alternatives, Power, Data Interpretation, Statistical, Urinary Tract Infections, Medicine, Poisson Mixed Model, Female, Medical Informatics, Zero-inflation
Poisson Regression, Statistics & Probability, Score Test, 310, Models, Biological, Environmental & Occupational Health, 230203 Statistical Theory, Cohort Studies, 780101 Mathematical sciences, C1, Models, Risk Factors, Homes for the Aged, Humans, Computer Simulation, Poisson Distribution, Aged, Retrospective Studies, Models, Statistical, 230204 Applied Statistics, Research & Experimental, 0104 Statistics, Count Data, Public, Middle Aged, Alternatives, Power, Data Interpretation, Statistical, Urinary Tract Infections, Medicine, Poisson Mixed Model, Female, Medical Informatics, Zero-inflation
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