The Ridge-Hurdle Negative Binomial Regression Model: A Novel Solution for Zero-Inflated Counts in the Presence of Multicollinearity

Nayem Hm () and B. M. Golam Kibria
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Nayem Hm: Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA
B. M. Golam Kibria: Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA

Stats, 2025, vol. 8, issue 4, 1-21

Abstract: Datasets with many zero outcomes are common in real-world studies and often exhibit overdispersion and strong correlations among predictors, creating challenges for standard count models. Traditional approaches such as the Zero-Inflated Poisson (ZIP), Zero-Inflated Negative Binomial (ZINB), and Hurdle models can handle extra zeros and overdispersion but struggle when multicollinearity is present. This study introduces the Ridge-Hurdle Negative Binomial model, which incorporates L 2 regularization into the truncated count component of the hurdle framework to jointly address zero inflation, overdispersion, and multicollinearity. Monte Carlo simulations under varying sample sizes, predictor correlations, and levels of overdispersion and zero inflation show that Ridge-Hurdle NB consistently achieves the lowest mean squared error (MSE) compared to ZIP, ZINB, Hurdle Poisson, Hurdle Negative Binomial, Ridge ZIP, and Ridge ZINB models. Applications to the Wildlife Fish and Medical Care datasets further confirm its superior predictive performance, highlighting RHNB as a robust and efficient solution for complex count data modeling.

Keywords: multicollinearity; Ridge; zero-inflated; MSE; Ridge-Hurdle Negative Binomial (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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