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
exaly +6 more sources
OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION
Barekeng, 2023 Statistical analysis that can be used if the response variable is quantified data is Poisson regression, assuming that the assumption must be met equidispersion, where the average response variable is the same as the standard deviation value.
Yesan Tiara +3 more
doaj +4 more sources
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 +5 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
doaj +4 more sources
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
doaj +4 more sources
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
doaj +3 more sources
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
doaj +2 more sources
Barekeng, 2022 Traffic accidents that always increase along with the increasing population growth and the number of vehicles impact the national economy. The number of traffic accidents is a count data that a Poisson distribution can approximate. The Poisson regression
M Al Haris, Prizka Rismawati Arum
doaj +3 more sources
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
core +5 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
doaj +3 more sources