A new mixed‐effects regression model for the analysis of zero‐modified hierarchical count data

• Published in: Biometrical journal Vol. 63; no. 1; pp. 81 - 104
• Main Authors: Bertoli, Wesley, Conceição, Katiane S., Andrade, Marinho G., Louzada, Francisco
• Format: Journal Article
• Language: English
• Published: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.01.2021
• Subjects: Adaptive algorithms; approximate Bayesian inference; Bayesian analysis; Clustering; Competitiveness; Correlation analysis; Data structures; Datasets; Divergence; Empirical analysis; Heterogeneity; Mathematical models; mixed‐effects hurdle model; Monte Carlo simulation; overdispersion; Parameter estimation; Parameter sensitivity; Poisson distribution; Regression analysis; Regression models; repeated measures; Sensitivity analysis; Statistical analysis; Within-subjects design; zero‐modified count data; approximate Bayesian inference; mixed-effects hurdle model; overdispersion; zero-modified count data; Monte Carlo simulation; repeated measures

Count data sets are traditionally analyzed using the ordinary Poisson distribution. However, such a model has its applicability limited as it can be somewhat restrictive to handle specific data structures. In this case, it arises the need for obtaining alternative models that accommodate, for exampl...