Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model [PDF]
BMC Infectious DiseasesBackground 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, an indicator of how large of a keyword is in Baidu’s search volume, in ...
Wanwan Zhou +10 more
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A Novel Phylogenetic Negative Binomial Regression Model for Count-Dependent Variables [PDF]
Biology, 2023 Regression models are extensively used to explore the relationship between a dependent variable and its covariates. These models work well when the dependent variable is categorical and the data are supposedly independent, as is the case with generalized
Dwueng-Chwuan Jhwueng, Chi-Yu Wu
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Improved estimation in negative binomial regression. [PDF]
Stat Med, 2022 AbstractNegative binomial regression is commonly employed to analyze overdispersed count data. With small to moderate sample sizes, the maximum likelihood estimator of the dispersion parameter may be subject to a significant bias, that in turn affects inference on mean parameters.
Kenne Pagui EC, Salvan A, Sartori N.
europepmc +4 more sources
Double Generalized Beta-Binomial and Negative Binomial Regression Models
Revista Colombiana de Estadística, 2017 Overdispersion is a common phenomenon in count datasets, that can greatly affect inferences about the model. In this paper develop three joint mean and dispersion regression models in order to fit overdispersed data.
EDILBERTO CEPEDA-CUERVO +1 more
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Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression [PDF]
Entropy, 2021 This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance.
Shuai Sun +3 more
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NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis [PDF]
BMC Bioinformatics, 2020 Background Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across ...
Jing Zhang +6 more
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New two parameter hybrid estimator for zero inflated negative binomial regression models [PDF]
Scientific ReportsThe 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
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Odds ratios from logistic, geometric, Poisson, and negative binomial regression models [PDF]
BMC Medical Research Methodology, 2018 Background The odds ratio (OR) is used as an important metric of comparison of two or more groups in many biomedical applications when the data measure the presence or absence of an event or represent the frequency of its occurrence.
Christopher J. Sroka +1 more
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Lognormal and Gamma Mixed Negative Binomial Regression. [PDF]
Proc Int Conf Mach Learn, 2012 In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson ...
Zhou M, Li L, Dunson D, Carin L.
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Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression [PDF]
Genome Biology, 2019 Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects.
Christoph Hafemeister, Rahul Satija
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