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

oleh: Luiz Paulo Lopes Fávero, Alexandre Duarte, Helder Prado Santos

Format: Article
Diterbitkan: MDPI AG 2024-03-01

Deskripsi

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.