Fávero, L.P.L.; Duarte, A.; Santos, H.P. A New Computational Algorithm for Assessing Overdispersion and Zero-Inflation in Machine Learning Count Models with Python. Computers2024, 13, 88.
Fávero, L.P.L.; Duarte, A.; Santos, H.P. A New Computational Algorithm for Assessing Overdispersion and Zero-Inflation in Machine Learning Count Models with Python. Computers 2024, 13, 88.
Fávero, L.P.L.; Duarte, A.; Santos, H.P. A New Computational Algorithm for Assessing Overdispersion and Zero-Inflation in Machine Learning Count Models with Python. Computers2024, 13, 88.
Fávero, L.P.L.; Duarte, A.; Santos, H.P. A New Computational Algorithm for Assessing Overdispersion and Zero-Inflation in Machine Learning Count Models with Python. Computers 2024, 13, 88.
Abstract
Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. 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 criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
