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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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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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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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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Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and negative binomial regression [PDF]
Health Psychology and Behavioral Medicine, 2021 James A Green
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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
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POISSON REGRESSION MODELS TO ANALYZE FACTORS THAT INFLUENCE THE NUMBER OF TUBERCULOSIS CASES IN JAVA
Barekeng, 2023 Tuberculosis is an infectious disease and one of the world's top 10 highest causes of mortality in Indonesia. Based on this fact, it is necessary to study what factors affect number of tuberculosis cases.
Yekti Widyaningsih +1 more
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