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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The Negative Binomial regression
Southwest Respiratory and Critical Care Chronicles, 2015 Shengping Yang, Gilbert Berdine
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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
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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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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Perbandingan Regresi Binomial Negatif dan Regresi Conway-Maxwell-Poisson dalam Mengatasi Overdispersi pada Regresi Poisson [PDF]
Jurnal Gantang, 2017 Regresi Binomial Negatif dan regresi Conway-Maxwell-Poisson merupakan solusi untuk mengatasi overdispersi pada regresi Poisson. Kedua model tersebut merupakan perluasan dari model regresi Poisson.
Lusi Eka Afri
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Fast Bayesian Variable Selection in Binomial and Negative Binomial Regression [PDF]
, 2021 18 pages; this work is superseded by arXiv:2208 ...
Martin Jankowiak
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Disease Mapping via Negative Binomial Regression M-quantiles [PDF]
Statistics in Medicine, 2013 We introduce a semi-parametric approach to ecological regression for disease mapping, based on modelling the regression M-quantiles of a Negative Binomial variable. The proposed method is robust to outliers in the model covariates, including those due to
Chambers, Ray +2 more
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