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

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...

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Vydáno v:Computers (Basel) Ročník 13; číslo 4; s. 88
Hlavní autoři: Fávero, Luiz Paulo Lopes, Duarte, Alexandre, Santos, Helder Prado
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2024
Témata:
Algorithms
count data
Data analysis
Data mining
Dispersion measures (Statistics)
Generalized linear models
Likelihood ratio
Machine learning
Methods
negative binomial regression
overdispersion
Poisson regression
Programming languages
Python
Python (Programming language)
Statistical tests
Variables
Brazil
ISSN:2073-431X, 2073-431X
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers13040088