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 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 Spatio-temporal modelling of tick life-stage count data with spatially varying coefficients

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Thabo Lephoto ◽  
Henry Mwambi ◽  
Oliver Bodhlyera ◽  
Holly Gaff
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Life Stage ◽  
Poisson Model ◽  
Information Criteria ◽  
Count Data Models ◽  
Varying Coefficients ◽  
Time Interaction ◽  
York County ◽  
Spatio Temporal

There is a vast amount of geo-referenced data in many fields of study including ecological studies. Geo-referencing is usually by point referencing; that is, latitudes and longitudes or by areal referencing, which includes districts, counties, states, provinces and other administrative units. The availability of large geo-referenced datasets for modelling has necessitated the development and application of spatial statistical methods. However, spatial varying coefficients models exploring the abundance of tick counts remain limited. In this study we used data that was collected and prepared by researchers in the Department of Biological Sciences from the Old Dominion University, Virginia, USA. We modelled tick life-stage counts and abundance variability from 12 sampling locations, with 5 different habitats (numbered 1-5), three habitat types; namely: woods, edges and grass; collected monthly from May 2009 through December 2018. Spatio-temporal Poisson and spatio-temporal negative binomial (NB) count data models were fitted to the data and compared using the deviance information criteria (DIC). The NB model outperformed the Poisson models with all its DIC values being smaller than those of the Poisson model. Results showed that the covariates varied spatially across counties. There was a decreasing time (in years) effect over the study period. However, even though the time effect was decreasing over the study period, space-time interaction effects were seen to be increasing over time in York County.

Comparison of Different Count Models for Investigation of Some Environmental Factors Affecting Stillbirth in Holsteins

2022 ◽  
Author(s):  
Y. Gevrekçi ◽  
Ö.İ. Güneri ◽  
Ç. Takma ◽  
A. Yeşilova
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Negative Binomial ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Count Data Models ◽  
Factors Affecting ◽  
Independent Variables ◽  
Gender Parity ◽  
Hurdle Regression

Background: The objective of this study is comparing different count data models for stillbirth data. In modeling this type of data, Poisson regression or alternative models can be preferred. Methods: The poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, poisson-logit hurdle and negative binomial-logit hurdle regressions were compared and used to examine the effects of the gender, parity and herd-year-season independent variables on stillbirth. Furthermore, the Log-Likelihood statistics, Akaike Information Criteria, Bayesian Information Criteria and rootogram graphs were used as comparison criteria for performance of the models. According to these criteria, Negative Binomial-Logit Hurdle Regression model was chosen as the best model. Result: The parameter estimates obtained by Negative Binomial-Logit Hurdle Regression model in relation to the effects of the gender, parity and herd-year-season independent variables on stillbirth were found to be significant (p less than 0.01). It was found that while stillbirth incidence was higher in males than females, it was found to decrease as the parity increased. As a result, the Negative Binomial Logit Hurdle model was found the best model for stillbirth count data with overdispersion.

scholarly journals What Might Drive Block Ownership In Canadian Firms? Evidence Through Count Data Models

2013 ◽  
Vol 29 (4) ◽  
pp. 1049 ◽  
Author(s):  
Malek Saihi ◽  
Amel Belanes
Keyword(s):  
Ownership Structure ◽  
Count Data ◽  
Negative Binomial ◽  
Research Topic ◽  
Free Cash Flow ◽  
Dynamic Nature ◽  
Count Data Models ◽  
Concentrated Ownership ◽  
High Level ◽  
Binomial Models

This paperaddresses the following crucial question: What might influence the decision of a blockholder to enteror exit a firm? Within a concentrated ownership context, such as Canada, we investigate impact of corporategovernance, firms risk, debt, liquidity and size on increase in number ofblockholders. This studycontributes to research topic by shedding light onendogeneity and dynamic nature of ownership structure. In order to take intoaccount discreteness of additional blockholders number, we used four CountData models, namely Poisson, Negative Binomial,Zero-Inflated Poisson, and Zero-Inflated Negative Binomial models. Ourfindings show that while excessive high level of risk, free cash-flow, firmsize, institutional and insiders ownership dissuade shareholders from gettinglarge stakes, moderately high levels of risk and firm leverage encourage themto join Blockholders list.

scholarly journals Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss
Keyword(s):  
Simulation Study ◽  
Count Data ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Negative Binomial Regression ◽  
Binary Outcomes ◽  
Small Scale ◽  
Panel Count Data ◽  
Count Data Models ◽  
Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB 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.

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