Results 1 to 10 of about 12,857 (287)

Zero inflated negative binomial-generalized exponential distributionand its applications [PDF]

Songklanakarin Journal of Science and Technology (SJST), 2014
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, Winai Bodhisuwan, Thidaporn Supapakorn   +2 more
doaj   +2 more sources

New two parameter hybrid estimator for zero inflated negative binomial regression models [PDF]

Scientific Reports
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, M. Nagy, Ali T. Hammad, A. H. Mansi, Getachew Tekle Mekiso, M. M. Abd El-Raouf   +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]

BMC Infect Dis
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, Huang D, Liang Q, Huang T, Wang X, Pei H, Chen S, Liu L, Wei Y, Qin L, Xie Y.   +10 more
europepmc   +4 more sources

Modeling sparse Rift Valley fever incidence data: a Bayesian perspective on zero-inflated self-exciting and autoregressive models [PDF]

BMC Infectious Diseases
Background Rift Valley fever (RVF) is a mosquito-borne zoonotic disease for which predictive modeling is often hindered by sparse data, particularly the high frequency of zero counts in both human and livestock surveillance systems.
Alexandros Angelakis, Bryan O. Nyawanda, Penelope Vounatsou   +2 more
doaj   +2 more sources

Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model [PDF]

International Journal of Environmental Research and Public Health, 2021
Kyung Im Kang, Kyonghwa Kang, Chanhee Kim   +2 more
exaly   +2 more sources

Distribution of clones among hosts for the lizard malaria parasite Plasmodium mexicanum [PDF]

PeerJ, 2021
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]

Tutorials in Quantitative Methods for Psychology, 2023
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, Thennarasu, K., Philip, Mariamma, Jaisoorya, T. S.   +3 more
doaj   +1 more source

A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa [PDF]

International Journal of Environmental Research and Public Health, 2019
Gbenga J Abiodun, Olusola Samuel Makinde, Abiodun M Adeola   +2 more
exaly   +2 more sources

Neutrosophic Negative Binomial Hurdle Models for Overdispersed Zero-Inflated Count Data

Transactions on Fuzzy Sets and Systems
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

Evaluating the performance of different Bayesian count models in modelling childhood vaccine uptake among children aged 12–23 months in Nigeria

BMC Public Health, 2023
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, T. V. Lawal, K. A. Atoloye   +2 more
doaj   +1 more source