scholarly journals Bayesian hierarchical negative binomial models for multivariable analyses with applications to human microbiome count data

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0220961
Author(s):  
Amanda H. Pendegraft ◽  
Boyi Guo ◽  
Nengjun Yi
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Human Microbiome ◽  
Bayesian Hierarchical ◽  
Multivariable Analyses ◽  
Negative Binomial Models ◽  
Binomial Models

scholarly journals An Application Comparison of Two Negative Binomial Models on Rainfall Count Data

2021 ◽  
Vol 1818 (1) ◽  
pp. 012100
Author(s):  
L. H. Hashim ◽  
N. K. Dreeb ◽  
K. H. Hashim ◽  
Mushtak A. K. Shiker
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Models ◽  
Binomial Models

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

scholarly journals An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data

2017 ◽  
Vol 47 (6) ◽  
pp. 1722-1738 ◽  
Author(s):  
Elizabeth H. Payne ◽  
Mulugeta Gebregziabher ◽  
James W. Hardin ◽  
Viswanathan Ramakrishnan ◽  
Leonard E. Egede
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Empirical Approach ◽  
Negative Binomial Models ◽  
Binomial Models

scholarly journals Modeling the Frequency of Auto Insurance Claims by Means of Poisson and Negative Binomial Models

2015 ◽  
Vol 62 (2) ◽  
pp. 151-168 ◽  
Author(s):  
Mihaela David ◽  
Dănuţ-Vasile Jemna
Keyword(s):  
Risk Factors ◽  
Count Data ◽  
Negative Binomial ◽  
Information Criteria ◽  
Count Data Models ◽  
Auto Insurance ◽  
Insurance Portfolio ◽  
Negative Binomial Models ◽  
Degree Of Risk ◽  
Binomial Models

Abstract Within non-life insurance pricing, an accurate evaluation of claim frequency, also known in theory as count data, represents an essential part in determining an insurance premium according to the policyholder’s degree of risk. Count regression analysis allows the identification of the risk factors and the prediction of the expected frequency of claims given the characteristics of policyholders. The aim of this paper is to verify several hypothesis related to the methodology of count data models and also to the risk factors used to explain the frequency of claims. In addition to the standard Poisson regression, Negative Binomial models are applied to a French auto insurance portfolio. The best model was chosen by means of the log-likelihood ratio and the information criteria. Based on this model, the profile of the policyholders with the highest degree of risk is determined

scholarly journals Criminal Justice Debt During the Prisoner Reintegration Process: Who Has It and How Much?

2018 ◽  
Vol 46 (1) ◽  
pp. 154-172 ◽  
Author(s):  
Nathan W. Link
Keyword(s):  
Criminal Justice ◽  
Negative Binomial ◽  
Policy Implications ◽  
Community Supervision ◽  
Individual Level ◽  
Prisoner Reintegration ◽  
Negative Binomial Models ◽  
The Individual ◽  
Binomial Models ◽  
Transition Home

Much recent, national attention has centered on financial sanctions and associated debt burdens related to criminal justice. Scholars and practitioners alike have argued that financial debt among the incarcerated, in particular, exacerbates a transition home already defined by difficulties. This article takes a step back and assesses who is at risk of these adverse consequences in reentry by examining the extent of debt burdens that resulted from financial sanctions, its sources, and the individual-level factors that are associated with owing criminal justice debt. Relying on the Returning Home data ( N = 740), results from descriptive analyses, logistic regression, and negative binomial models show that a large proportion of respondents owed debts and that debt was strongly linked with being mandated to community supervision. In addition, debt amount was predicted by employment, income, and race. Policy implications in the realm of financial sanctioning by courts and correctional agencies are discussed.

scholarly journals An efficient EM algorithm for the mixture of negative binomial models

2019 ◽  
Vol 1324 ◽  
pp. 012093
Author(s):  
Chunmao Huang ◽  
Xingwang Liu ◽  
Tianyuan Yao ◽  
Xiaoqiang Wang
Keyword(s):  
Em Algorithm ◽  
Negative Binomial ◽  
Negative Binomial Models ◽  
Binomial Models

Do Denser Neighborhoods Have Safer Streets? Population Density and Traffic Safety in the Philadelphia Region

2019 ◽  
pp. 0739456X1984504 ◽  
Author(s):  
Erick Guerra ◽  
Xiaoxia Dong ◽  
Michelle Kondo
Keyword(s):  
Population Density ◽  
Traffic Safety ◽  
Urban Form ◽  
Urban Areas ◽  
Negative Binomial ◽  
Pedestrian Safety ◽  
Negative Binomial Models ◽  
The Relationship ◽  
Binomial Models ◽  
Density Results

This study uses multilevel negative binomial models to investigate relationships between neighborhood socio-demographics, urban form, roadway characteristics, traffic collisions, injuries, and fatalities on the Philadelphia region’s streets from 2010 to 2014. We pay particular attention to neighborhood population density. Results indicate that streets in denser neighborhoods have fewer overall collisions, injuries, and fatalities. The association with pedestrian safety is mixed and somewhat uncertain across urban areas and model specifications. This study highlights the importance of population density in traffic safety and helps explain some of the variation in findings across studies examining the relationship between urban form and pedestrian safety.

scholarly journals Forecasting asthma-related hospital admissions in London using negative binomial models

2013 ◽  
Vol 10 (2) ◽  
pp. 85-94 ◽  
Author(s):  
Ireneous N Soyiri ◽  
Daniel D Reidpath ◽  
Christophe Sarran
Keyword(s):  
Negative Binomial ◽  
Hospital Admissions ◽  
Negative Binomial Models ◽  
Binomial Models
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