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This content will become publicly available on June 30, 2026

Title: A Comparison of Zero-Inflated Models for Modern Biomedical Data
There has been a growing number of datasets exhibiting an excess of zero values that cannot be adequately modeled using standard probability distributions. For example, microbiome data and single-cell RNA sequencing data consist of count measurements in which the proportion of zeros exceeds what can be captured by standard distributions such as the Poisson or negative binomial, while also requiring appropriate modeling of the nonzero counts. Several models have been proposed to address zero-inflated datasets including the zero-inflated negative binomial, hurdle negative binomial model, and the truncated latent Gaussian copula model. This study aims to compare various models and determine which one performs optimally under different conditions using both simulation studies and real data analyses. We are particularly interested in investigating how dependence among the variables, level of zeroinflation or deflation, and variance of the data affects model selection. KEYWORDS: Zero-InflatedModels; HurdleModels; Truncated Latent Gaussian CopulaModel; Microbiome Data; Gene-Sequencing Data; Zero-Inflation, Negative Binomial; Zero-Deflation  more » « less
Award ID(s):
2150179
PAR ID:
10611607
Author(s) / Creator(s):
; ;
Publisher / Repository:
American Journal of Undergraduate Research
Date Published:
Journal Name:
American Journal of Undergraduate Research
Volume:
22
Issue:
2
ISSN:
1536-4585
Page Range / eLocation ID:
49-68
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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