Zero inflated negative binomial-generalized exponential distributionand its applications
In this paper, we propose a new zero inflated distribution, namely, the zero inflated negative binomial-generalized exponential (ZINB-GE) distribution. The new distribution is used for count data with extra zeros and is an alternative for data analysis with over-dispersed count data.
Sirinapa Aryuyuen +2 more
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Zero Inflated Poisson regression (ZIP) and Zero Inflated Negative Binomial (ZINB) regression were used if there was overdispersion and no multicollinearity in the data. This study aims to analyze measles in Bengkulu Province using the ZIP and ZINB regression models. Among them are selecting the best model, seeing the influential variables from the best
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A bivariate zero-inflated negative binomial model and its applications to biomedical settings. [PDF]
Cho H, Liu C, Preisser JS, Wu D.
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Factors associated with scientific production citations in dentistry: Zero-inflated negative binomial regression and hurdle modelling. [PDF]
Millones-Gómez PA +4 more
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Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial. [PDF]
Lee KH +4 more
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Infants' gut microbiome data: A Bayesian Marginal Zero-inflated Negative Binomial regression model for multivariate analyses of count data. [PDF]
Hajihosseini M +3 more
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Exponentially weighted moving average chart using zero-inflated negative binomial distribution
Zero-inflated models are frequently used to deal with data having many zeros. A commonly used model for over-dispersed data containing zeros is known as the zero-inflated Poisson model. However, to account for the heterogeneity of counts that leads to excess variance besides inflation of zeros in the data using a more flexible model than the zero ...
Ali Abbas, Sajid Ali, Ismail Shah
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Risk Factors Influencing the Severity of Cognitive Decline in Elderly Taxi Drivers Over 65 Years of Age: Applying a Zero-Inflated Negative Binomial Regression Model. [PDF]
Ok JS, Kim H, Kang K, Myong JP, Heo JH.
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A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data. [PDF]
Jiang S +5 more
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Normalizing Metagenomic Hi-C Data and Detecting Spurious Contacts Using Zero-Inflated Negative Binomial Regression. [PDF]
Du Y, Laperriere SM, Fuhrman J, Sun F.
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