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.

Effects of Imprisonment and Community Supervision on Neighborhood Political Participation in North Carolina

2013 ◽  
Vol 651 (1) ◽  
pp. 184-201 ◽  
Author(s):  
Traci R. Burch
Keyword(s):  
North Carolina ◽  
Criminal Justice ◽  
Political Participation ◽  
Voter Turnout ◽  
Census Bureau ◽  
Community Supervision ◽  
Individual Level ◽  
Probation And Parole ◽  
The Individual ◽  
Analyze Data

This article considers the effect of prison, probation, and parole on neighborhood political participation in North Carolina. I analyze data from state boards of elections, departments of corrections, departments of public health, the Census Bureau, and market research firms for 2000 and 2008. Multivariate regressions reveal a complex relationship between criminal justice supervision and voter turnout. The evidence suggests that at the individual level and in the aggregate, the criminal justice system shapes neighborhood political participation.

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 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

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 Estimated hospitalizations attributed to norovirus and rotavirus infection in Canada, 2006–2010

2015 ◽  
Vol 143 (16) ◽  
pp. 3528-3537 ◽  
Author(s):  
V. K. MORTON ◽  
M. K. THOMAS ◽  
S. A. McEWEN
Keyword(s):  
Negative Binomial ◽  
Comprehensive Analysis ◽  
Administrative Databases ◽  
Total Annual Cost ◽  
Economic Costs ◽  
The Mean ◽  
Negative Binomial Models ◽  
Binomial Models ◽  
Canadian Healthcare System ◽  
Canadian Healthcare

SUMMARYEnteric viruses including norovirus and rotavirus are leading causes of gastroenteritis in Canada. However, only a small number of clinical cases are actually tested for these pathogens leading to systematic underestimation of attributed hospitalizations in administrative databases. The objective of this analysis was to estimate the number of hospitalizations due to norovirus and rotavirus in Canada. Hospitalization records for acute gastroenteritis-associated discharges at all acute-care hospitals in Canada between 2006 and 2011 were analysed. Cause-unspecified gastroenteritis hospitalizations were modelled using age-specific negative binomial models with cause-specified gastroenteritis admissions as predictors. The coefficients from the models were used to estimate the number of norovirus and rotavirus admissions. The total annual hospitalizations for rotavirus were estimated to be between 4500 and 10 000. Total annual hospitalizations for norovirus were estimated to be between 4000 and 11 000. The mean total annual cost associated with these hospitalizations was estimated to be at least $16 million for rotavirus and $21 million for norovirus (all figures in Canadian dollars). This study is the first comprehensive analysis of norovirus and rotavirus hospitalizations in Canada. These estimates provide a more complete assessment of the burden and economic costs of these pathogens to the Canadian healthcare system.

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