Introduction to optimization methods and their application in statistics
Introduction to optimization methods and their application in statistics
Overdispersion: models and estimation
Computational Statistics & Data Analysis
On estimation of the Poisson parameter in zero-modified Poisson models
Computational Statistics & Data Analysis
A score test for zero-inflation in multilevel count data
Computational Statistics & Data Analysis
Tests for zero-inflation and overdispersion: A new approach based on the stochastic convex order
Computational Statistics & Data Analysis
Score tests for zero-inflated generalized Poisson mixed regression models
Computational Statistics & Data Analysis
Sieve maximum likelihood estimation for doubly semiparametric zero-inflated Poisson models
Journal of Multivariate Analysis
An extension of an over-dispersion test for count data
Computational Statistics & Data Analysis
Score tests for zero-inflation and overdispersion in two-level count data
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
In many situations count data have a large proportion of zeros and the zero-inflated Poisson regression (ZIP) model may be appropriate. A simple score test for zero-inflation, comparing the ZIP model with a constant proportion of excess zeros to a standard Poisson regression model, was given by van den Broek (Biometrics, 51 (1995) 738-743). We extend this test to the more general situation where the zero probability is allowed to depend on covariates. The performance of this test is evaluated using a simulation study. To identify potentially important covariates in the zero-inflation model a composite test is proposed. The use of the general score test and the composite procedure is illustrated on two examples from the literature. The composite score test is found to suggest appropriate models.