New two parameter hybrid estimator for zero inflated negative binomial regression models [PDF]
The zero-inflated negative binomial regression (ZINBR) model is used for modeling count data that exhibit both overdispersion and zero-inflated counts. However, a persistent challenge in the efficient estimation of parameters within ZINBR models is the ...
Fatimah A. Almulhim +5 more
doaj +2 more sources
Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model. [PDF]
Abstract Background It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index (BSI) in the early warning and predicting the epidemic trend of COVID-19.
Zhou W +10 more
europepmc +4 more sources
Zero inflated negative binomial-generalized exponential distributionand its applications [PDF]
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
Sirinapa Aryuyuen +2 more
doaj +1 more source
Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model [PDF]
Chanhee Kim +2 more
exaly +2 more sources
A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa [PDF]
Joel Botai +2 more
exaly +2 more sources
Distribution of clones among hosts for the lizard malaria parasite Plasmodium mexicanum [PDF]
Background Malaria parasites reproduce asexually, leading to the production of large numbers of genetically identical parasites, here termed a clonal line or clone.
Allison T. Neal
doaj +2 more sources
Regression models for count data with excess zeros: A comparison using survey data [PDF]
Presence of excess zeros and the distributions are major concern in modeling count data. Zero inflated and hurdle models are regression techniques which can handle zero inflated count data.
Bhaskar, Adhin +3 more
doaj +1 more source
Background Choosing appropriate models for count health outcomes remains a challenge to public health researchers and the validity of the findings thereof.
A. F. Fagbamigbe +2 more
doaj +1 more source
Neutrosophic Negative Binomial Hurdle Models for Overdispersed Zero-Inflated Count Data
The Negative Binomial Hurdle (NBH) model stands as a preeminent statistical framework for analyzing overdispersed count data characterized by a preponderance of zero observations.
Ibrahim Sadok
doaj +2 more sources
This article presents the results of the implementation of a forecasting model, to predict the relief materials needed for assisting in decisions prior to natural disasters, thus filling a gap in the exploration of Generalized Linear Mixed Models (GLMM ...
Camila Pareja Yale +2 more
doaj +1 more source

