A New Computational Algorithm for Assessing Overdispersion and Zero-Inflation in Machine Learning Count Models with Python

Abstract

This article provides an overview of count data and count models, explores zero inflation, introduces likelihood ratio tests, and explains how the Vuong test can be used as a model selection criterion for assessing overdispersion. The motivation of this work was to create a Vuong test implementation from scratch using the Python programming language. This implementation supports our objective of enhancing the accessibility and applicability of the Vuong test in real-world scenarios, providing a valuable contribution to the academic community, since Python did not have an implementation of this statistical test.

Keywords

• count data
• machine learning
• negative binomial regression
• overdispersion
• Poisson regression
• Python