No Zero Left Behind: Comparing the Fit for Zero-Inflation Models as a Function of Skew and Proportion of Zeros
by Jeffrey M. Miller, M. David Miller.
Several models have been proposed for analyzing data characterized by a preponderance of zeros.
Substantively, the choice between these models should be based solely on the data generating process.
However, datasets can vary as a function of both the proportions of zeros and the distribution for the nonzeros.
A Monte Carlo design was used to sample 1,000 cases from each of six distributions and five proportions of zeros.
Between-model superiority was tested using deviance statistics and AICs over 2,000 simulations per condition.
The results suggest that the best-fitting zero-inflated model sometimes depends on the proportion of zeros
and the distribution for the nonzeros. In fact, there are situations where the zero-inflated models are not necessary.
Zero-Inflation, ZIP, Hurdle, Zero-Inflated Poisson, Poisson
Jeffrey M. Miller, email@example.com
M. David Miller, firstname.lastname@example.org
Joseph W. McKean, email@example.com
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