International Journal of Mathematics Trends and Technology
Volume 65 | Issue 8 | Year 2019 | Article Id. IJMTT-V65I8P516 | DOI : https://doi.org/10.14445/22315373/IJMTT-V65I8P516
Many tests for the analysis of continuous data have the underlying assumption that the data in question follows a normal distribution (ex. ANOVA, regression, etc.). Within certain research topics, it is common to end up with a dataset that has a disproportionately high number of zero-values but otherwise might follow a normal or a Poisson distribution. These datasets are often referred to as ‘zero-inflated’ and their analysis can be challenging. An example of where these zero-inflated datasets arise is in plant science. We conducted a simulation study to compare the performance of the Poisson zero-inflated model to a standard ANOVA model and also to a regular Poisson model on different types of zero-inflated data. Underlying distributions, number of populations, sample sizes, and percentages of zeros were variables of consideration. In this study, we conduct a Type I error assessment followed by a power comparison between the models.
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Lucas Young, Rhonda Magel, Curt Doetkott, "Type I Error Assessment And Power Comparison Of Anova And Zero-Inflated Methods On Zero-Inflated Data," International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no. 8, pp. 139-153, 2019. Crossref, https://doi.org/10.14445/22315373/IJMTT-V65I8P516
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