Passport Options: Continuous and Binomial Models

2010 ◽  
Author(s):  
Hamish Malloch ◽  
Peter W. Buchen
Keyword(s):  
Passport Options ◽  
Binomial Models

scholarly journals Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
Author(s):  
Lee ◽  
Guldmann ◽  
Choi
Keyword(s):  
Negative Binomial ◽  
Age Groups ◽  
Urban Environments ◽  
Vehicle Miles Traveled ◽  
Crash Frequency ◽  
Rural Environments ◽  
Lower Likelihood ◽  
Conditional Autoregressive ◽  
The Relationship ◽  
Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

p Value Adjustments for Multiple Tests in Multivariate Binomial Models

1989 ◽  
Vol 84 (407) ◽  
pp. 780 ◽  
Author(s):  
Peter H. Westfall ◽  
S. Stanley Young
Keyword(s):  
P Value ◽  
Multiple Tests ◽  
Binomial Models

Zapatista and counter-Zapatista protests

2012 ◽  
Vol 49 (3) ◽  
pp. 459-472 ◽  
Author(s):  
María Inclán
Keyword(s):  
Negative Binomial ◽  
Empirical Test ◽  
Party Competition ◽  
Public Works ◽  
Social Programs ◽  
Reciprocal Effect ◽  
Systematic Analysis ◽  
Binomial Models ◽  
Different Levels ◽  
Protest Activity

This study represents the first systematic analysis of the interactions between pro-Zapatista and counter-Zapatista protestors in Chiapas, Mexico, and the first empirical test of movement–countermovement theories in a transitional democracy. Three claims are tested: (1) movement protests trigger countermovement protest activity; (2) different political parties at different levels of government trigger movement–countermovement protest activity; and (3) victories won by one side of a conflict, viewed as procedural concessions, trigger further pro- and countermovement protest activity. These hypotheses are tested using negative binomial models and data on Zapatista-related protest activity between 1994 and 2003. The results show that: (1) movement and countermovement protests have a positive, reciprocal effect on both groups' future protest activity; (2) movement and countermovement protesting groups use the dominant political party as a target of protest. The characteristics of the electoral cycle and rise of multi-party competition at all levels of government do not have a consistent effect on protest activity; (3) granting procedural concessions to pro-movement actors generates more protest activity among both groups. However, granting procedural concessions via social programs and public works to the population irrespective of its sympathy to either side of the movement–countermovement conflict decreases movement protests and increases countermovement protests.

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

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.

A comparison of hierarchical models for relative catch efficiency based on paired-gear data for US Northwest Atlantic fish stocks

2013 ◽  
Vol 70 (9) ◽  
pp. 1306-1316 ◽  
Author(s):  
Timothy J. Miller
Keyword(s):  
Size Effects ◽  
Hierarchical Models ◽  
Northwest Atlantic ◽  
Fish Stocks ◽  
Gear Design ◽  
The Us ◽  
Conditional Beta ◽  
Binomial Models ◽  
Random Pair ◽  
Better Than

Selectivity and catch comparison studies are important for surveys that use two or more gears to collect relative abundance information. Prevailing model-based analytical methods for studies using a paired-gear design assume a binomial model for the data from each pair of gear sets. Important generalizations include nonparametric smooth size effects and normal random pair and size effects, but current methods for fitting models that account for random smooth size effects are restrictive, and observations within pairs may exhibit extra-binomial variation. I propose a hierarchical model that accounts for random smooth size effects among pairs and extra-binomial variation within pairs with a conditional beta-binomial distribution. I compared relative performance of models with different conditional distribution and random effects assumptions fit to data on 16 species from an experiment carried out in the US Northwest Atlantic Ocean comparing a new and a retiring vessel. For more than half of the species, conditional beta-binomial models performed better than binomial models, and accounting for random variation among pairs in the relative efficiency was important for all species.

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 Does Loneliness Have a Cost? A Population-Wide Study of the Association Between Loneliness and Healthcare Expenditure

2021 ◽  
Vol 66 ◽  
Author(s):  
Rachelle Meisters ◽  
Daan Westra ◽  
Polina Putrik ◽  
Hans Bosma ◽  
Dirk Ruwaard ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Healthcare Expenditure ◽  
Age Groups ◽  
Mental Healthcare ◽  
Multiple Sources ◽  
Related Factors ◽  
Younger Age ◽  
Unhealthy Behavior ◽  
Nationally Representative ◽  
Binomial Models

Objectives: Loneliness has been associated with unhealthy behavior, poorer health, and increased morbidity. However, the costs of loneliness are poorly understood.Methods: Multiple sources were combined into a dataset containing a nationally representative sample (n = 341,376) of Dutch adults (18+). The association between loneliness and total, general practitioner (GP), specialized, pharmaceutical, and mental healthcare expenditure was tested using Poisson and Zero-inflated negative binomial models, controlling for numerous potential confounders (i.e., demographic, socioeconomic, lifestyle-related factors, self-perceived health, and psychological distress), for four age groups.Results: Controlling for demographic, socioeconomic, and lifestyle-related factors, loneliness was indirectly (via poorer health) associated with higher expenditure in all categories. In fully adjusted models, it showed a direct association with higher expenditure for GP and mental healthcare (0.5 and 11.1%, respectively). The association with mental healthcare expenditure was stronger in younger than in older adults (for ages 19–40, the contribution of loneliness represented 61.8% of the overall association).Conclusion: Loneliness contributes to health expenditure both directly and indirectly, particularly in younger age groups. This implies a strong financial imperative to address this issue.

scholarly journals Fast effect size shrinkage software for beta-binomial models of allelic imbalance

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 2024
Author(s):  
Joshua P. Zitovsky ◽  
Michael I. Love
Keyword(s):  
Allelic Imbalance ◽  
Real Data ◽  
Shrinkage Estimators ◽  
Data Set ◽  
Bayesian Shrinkage ◽  
In Cis ◽  
Posterior Estimation ◽  
Binomial Models ◽  
Better Than ◽  
Diploid Organism

Allelic imbalance occurs when the two alleles of a gene are differentially expressed within a diploid organism and can indicate important differences in cis-regulation and epigenetic state across the two chromosomes. Because of this, the ability to accurately quantify the proportion at which each allele of a gene is expressed is of great interest to researchers. This becomes challenging in the presence of small read counts and/or sample sizes, which can cause estimators for allelic expression proportions to have high variance. Investigators have traditionally dealt with this problem by filtering out genes with small counts and samples. However, this may inadvertently remove important genes that have truly large allelic imbalances. Another option is to use pseudocounts or Bayesian estimators to reduce the variance. To this end, we evaluated the accuracy of four different estimators, the latter two of which are Bayesian shrinkage estimators: maximum likelihood, adding a pseudocount to each allele, approximate posterior estimation of GLM coefficients (apeglm) and adaptive shrinkage (ash). We also wrote C++ code to quickly calculate ML and apeglm estimates and integrated it into the apeglm package. The four methods were evaluated on two simulations and one real data set. Apeglm consistently performed better than ML according to a variety of criteria, and generally outperformed use of pseudocounts as well. Ash also performed better than ML in one of the simulations, but in the other performance was more mixed. Finally, when compared to five other packages that also fit beta-binomial models, the apeglm package was substantially faster and more numerically reliable, making our package useful for quick and reliable analyses of allelic imbalance. Apeglm is available as an R/Bioconductor package at http://bioconductor.org/packages/apeglm.

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