Relevant bibliographies by topics / Negative binomial regression model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Negative binomial regression model'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Negative binomial regression model"

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Cepeda-Cuervo, Edilberto, and María Victoria Cifuentes-Amado. "Double Generalized Beta-Binomial and Negative Binomial Regression Models." Revista Colombiana de Estadística 40, no. 1 (January 16, 2017): 141–63. http://dx.doi.org/10.15446/rce.v40n1.61779.

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Overdispersion is a common phenomenon in count datasets, that can greatly affect inferences about the model. In this paper develop three joint mean and dispersion regression models in order to fit overdispersed data. These models are based on reparameterizations of the beta-binomial and negative binomial distributions. Finally, we propose a Bayesian approach to estimate the parameters of the overdispersion regression models and use it to fit a school absenteeism dataset.
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Famoye, Felix. "On the bivariate negative binomial regression model." Journal of Applied Statistics 37, no. 6 (May 11, 2010): 969–81. http://dx.doi.org/10.1080/02664760902984618.

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Xue, Dixi, and James A. Deddens. "Overdispersed negative binomial regression models." Communications in Statistics - Theory and Methods 21, no. 8 (January 1992): 2215–26. http://dx.doi.org/10.1080/03610929208830908.

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Li, Chin-Shang. "Semiparametric Negative Binomial Regression Models." Communications in Statistics - Simulation and Computation 39, no. 3 (February 24, 2010): 475–86. http://dx.doi.org/10.1080/03610910903480834.

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Hung, Lai-Fa. "A Negative Binomial Regression Model for Accuracy Tests." Applied Psychological Measurement 36, no. 2 (January 24, 2012): 88–103. http://dx.doi.org/10.1177/0146621611429548.

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Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.
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D’Andrea, Amanda, Ricardo Rocha, Vera Tomazella, and Francisco Louzada. "Negative Binomial Kumaraswamy-G Cure Rate Regression Model." Journal of Risk and Financial Management 11, no. 1 (January 19, 2018): 6. http://dx.doi.org/10.3390/jrfm11010006.

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Rashad, Nadwa Khazaal, Nawal Mahmood Hammood, and Zakariya Yahya Algamal. "Generalized ridge estimator in negative binomial regression model." Journal of Physics: Conference Series 1897, no. 1 (May 1, 2021): 012019. http://dx.doi.org/10.1088/1742-6596/1897/1/012019.

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Allison, Paul D., and Richard P. Waterman. "7. Fixed-Effects Negative Binomial Regression Models." Sociological Methodology 32, no. 1 (August 2002): 247–65. http://dx.doi.org/10.1111/1467-9531.00117.

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This paper demonstrates that the conditional negative binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true fixed-effects method. This method—which has been implemented in both Stata and LIMDEP—does not in fact control for all stable covariates. Three alternative methods are explored. A negative multinomial model yields the same estimator as the conditional Poisson estimator and hence does not provide any additional leverage for dealing with over-dispersion. On the other hand, a simulation study yields good results from applying an unconditional negative binomial regression estimator with dummy variables to represent the fixed effects. There is no evidence for any incidental parameters bias in the coefficients, and downward bias in the standard error estimates can be easily and effectively corrected using the deviance statistic. Finally, an approximate conditional method is found to perform at about the same level as the unconditional estimator.
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Faroughi, Pouya, and Noriszura Ismail. "Bivariate zero-inflated negative binomial regression model with applications." Journal of Statistical Computation and Simulation 87, no. 3 (July 28, 2016): 457–77. http://dx.doi.org/10.1080/00949655.2016.1213843.

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Türkan, Semra, and Gamze Özel. "A Jackknifed estimators for the negative binomial regression model." Communications in Statistics - Simulation and Computation 47, no. 6 (July 11, 2017): 1845–65. http://dx.doi.org/10.1080/03610918.2017.1327069.

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Dissertations / Theses on the topic "Negative binomial regression model"

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Zeileis, Achim, Christian Kleiber, and Simon Jackman. "Regression Models for Count Data in R." Foundation for Open Access Statistics, 2008. http://epub.wu.ac.at/4986/1/Zeileis_etal_2008_JSS_Regression%2DModels%2Dfor%2DCount%2DData%2Din%2DR.pdf.

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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inflated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences-better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (authors' abstract)
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Zeileis, Achim, Christian Kleiber, and Simon Jackman. "Regression Models for Count Data in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/1168/1/document.pdf.

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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of zero-inflated and hurdle regression models in the functions zeroinfl() and hurdle() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both model classes are able to incorporate over-dispersion and excess zeros - two problems that typically occur in count data sets in economics and the social and political sciences - better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Reineck, Viktor, and Folke Ulfsparre. "The Impact of Weather on Residential Fires in Sweden: A Regression Analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254304.

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The purpose of this report is to investigate possible relationships between the number of residential fires in Sweden and various weather parameters. The study is conducted based on a hypothesis as stated by the MSB, the Swedish Civil Contingencies Agency, that behavioral factors related to weather can have an influence on the number of residential fires. Generalized linear models within the regression analysis have been used and specifically Poisson and negative binomial regression. The aim was to map the possible connection and determine if it was possible to use the analysis as a tool to improve the emergency services in Sweden. Temperature, short term differences in temperature and precipitation were analyzed with residential fires as the dependent variable, which resulted in a model for each municipality in Sweden. The relationships between the weather parameters and residential fires, seen throughout Sweden, proved to be weak to non-existent with one exception. The average temperature variable was significant in 117 out of 290 municipalities and indicated a relationship where the expected number of residential fires decreases at temperature increases. Due to the weak relationships, the model is not recommended as a prognostic tool on a national level. However, individual models could be used as a supplement to current prognostic tools at a local level and used for preventive purposes. Thus, the study has concluded that weather has some impact on the expected number of residential fires and thus has the potential to be used as a tool when forecasting residential fires. As an addition to the regression analysis, an organizational analysis of the emergency services in Sweden is carried out. The analysis sought the optimal structure based on the emergency services conditions and requirements, which were defined on the basis of organizational concepts and methods. The result was a more structured operation and organization where methods and processes are managed at a centralized level.
Syftet med denna rapport är att undersöka eventuella samband mellan antalet bostadsbränder i Sverige och olika väderparametrar. Studien genomförts mot bakgrund av en hypotes ställd av MSB, Myndigheten för Samhällsskydd och Beredskap, om att beteendefaktorer relaterade till vädret kan ha en påverkan på antalet bostadsbränder. Generaliserade linjära modeller inom regressionsanalysen har använts och specifikt Poisson- och negativ binomialregression. Målet var att kartlägga det eventuella sambandet och avgöra huruvida det var möjligt att nyttja analysen som verktyg för att förbättra räddningstjänsten i Sverige. Temperatur, kortsiktig temperaturförändring och nederbörd analyserades med bostadsbränder som den beroende variabeln, vilket resulterade i en modell för varje svensk kommun. Sambanden mellan väderparametrarna och bostadsbränder, sett över hela Sverige, visade sig vara svaga till obefintliga med ett undantag. Variabeln för genomsnittstemperatur var signifikant i 117 av 290 kommuner och visade på ett samband där förväntat antal bostadsbränder minskar vid ökad temperatur. På grund av de svaga sambanden, sett över hela Sverige, rekommenderas inte modellen som prognostiskt verktyg på nationell nivå. Däremot skulle enskilda modeller kunna användas som komplement till nuvarande prognostiska verktyg på lokal nivå, samt användas i förebyggande syfte. Därmed har studien kommit fram till att väder har viss påverkan på det förväntade antalet bostadsbränder och således har potential att användas som verktyg vid prognos av bostadsbränder. Som ett komplement till regressionsanalysen genomförs en organisatorisk analys av räddningstjänsten i Sverige. Analysen sökte den optimala strukturen utifrån räddningstjänstens förutsättningar och krav, som definierades utifrån grundläggande organisatoriska begrepp och metoder. Resultatet blev en mer strukturerad verksamhet där metoder och processer sköts på en centraliserad nivå.
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Shrestha, Shivesh. "Development of structural condition thresholds for TSD measurements." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78039.

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This thesis presents (a) results of a field evaluation of the Traffic Speed Deflectometer (TSD) in the United States (b) deflection thresholds to classify the pavement structural condition obtained from the TSD for a small subset of the Pennsylvania secondary road network. The results of the field evaluation included: (1) repeatability of the TSD, (2) ability of the TSD to identify pavement sections with varying structural conditions, and (3) consistency between the structural number (SNeff) calculated from the TSD and SNeff calculated by the Pennsylvania Department of Transportation (PennDOT). The results showed consistent error standard deviation in the TSD measurements and that the TSD was able to identify pavement sections that varied in structural condition. Comparison of the SNeff calculated with TSD measurements, using an empirically developed equation by Rohde, with the SNeff calculated by PennDOT’s Pavement Management System based on construction history showed similar trends, although the TSD-calculated SNeff was higher. In order to develop deflection thresholds, a model that related the pavement surface condition to pavement surface age and structural condition was developed. Structural condition thresholds were then selected so that the pavement surface condition predicted from the model for a 10-year-old pavement surface fell within one of the three condition categories (Good, Fair, and Poor), to identify pavements in good, fair and poor condition. With Overall Pavement Index(OPI) characterizing the surface condition and Deflection Slope Index(DSI) characterizing the structural condition, the DSI threshold that separates structurally good from structurally fair pavements was determined as follows: (1) the OPI threshold that separates pavements with good surface condition from those with fair surface condition was obtained from the Pennsylvania Pavement Management System (PMS) and (2) the DSI thresholds were calculated using the determined OPI value and the model equation.
Master of Science
This thesis presents (a) some of the results of a field evaluation of the Traffic Speed Deflectometer (TSD) in the United States (b) deflection thresholds to classify the pavement structural condition obtained from the TSD for a small subset of the Pennsylvania secondary road network. The results of the field evaluation included: (1) repeatability of the TSD: which is the variation in repeated TSD measurements on the same section of the road, (2) ability of the TSD to identify pavement sections with varying structural conditions, and (3) consistency between the structural number (SNeff) calculated from the TSD and SNeff calculated by the Pennsylvania Department of Transportation (PennDOT). The pavement structural number is an abstract number expressing the structural strength of the pavement. The results showed that the TSD measurements were repeatable and that the TSD was able to identify pavement sections that varied in structural condition. Comparison of the SNeff calculated with TSD measurements, using an empirically developed equation by Rohde, with the SNeff calculated by PennDOT Pavement Management System based on construction history showed similar trends, although the TSD-calculated SNeff was higher. In order to develop deflection thresholds to categorize pavements in different condition: good, fair and poor, a model that related the pavement surface condition to pavement surface age and structural condition was developed. Structural condition thresholds were then selected so that the pavement surface condition predicted from the model for a 10-year-old pavement surface fell within one of the three condition categories (Good, Fair, and Poor), to identify pavements in good, fair and poor condition. With Overall Pavement Index(OPI) characterizing the surface condition and Deflection Slope Index(DSI) characterizing the structural condition, the DSI threshold that separates structurally good from structurally fair pavements was determined as follows: (1) the OPI threshold that separates pavements with good surface condition from those with fair surface condition was obtained from the Pennsylvania Pavement Management System (PMS) and (2) the DSI thresholds were calculated using the determined OPI value and the model equation.
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Pemmanaboina, Rajashekar. "Assessing Crash Occurrence On Urban Freeways Using Static And Dynamic Factors By Applying A System Of Interrelated Equations." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2617.

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Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3) safety models to determine the probability of crash occurrence, including a rainfall index that has been estimated using a logistic regression model. The study corridor is a 36.25 mile stretch of Interstate 4 in Central Florida. For the first two sections, crash cases from 1999 through 2002 were considered. Conventionally most of the crash frequency analysis model all crashes, instead of dividing them based on type of crash, peaking conditions, availability of light, severity, or pavement condition, etc. Also researchers traditionally used AADT to represent traffic volumes in their models. These two cases are examples of macroscopic crash frequency modeling. To investigate the microscopic models, and to identify the significant factors related to crash occurrence, a preliminary study (first analysis) explored the use of microscopic traffic volumes related to crash occurrence by comparing AADT/VMT with five to twenty minute volumes immediately preceding the crash. It was found that the volumes just before the time of crash occurrence proved to be a better predictor of crash frequency than AADT. The results also showed that road curvature, median type, number of lanes, pavement surface type and presence of on/off-ramps are among the significant factors that contribute to crash occurrence. In the second analysis various possible crash categories were prepared to exactly identify the factors related to them, using various roadway, geometric, and microscopic traffic variables. Five different categories are prepared based on a common platform, e.g. type of crash. They are: 1) Multiple and Single vehicle crashes, 2) Peak and Off-peak crashes, 3) Dry and Wet pavement crashes, 4) Daytime and Dark hour crashes, and 5) Property Damage Only (PDO) and Injury crashes. Each of the above mentioned models in each category are estimated separately. To account for the correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used, and then the models in each category were estimated simultaneously. SUNB estimation proved to be advantageous for two categories: Category 1, and Category 4. Road curvature and presence of On-ramps/Off-ramps were found to be the important factors, which can be related to every crash category. AADT was also found to be significant in all the models except for the single vehicle crash model. Median type and pavement surface type were among the other important factors causing crashes. It can be stated that the group of factors found in the model considering all crashes is a superset of the factors that were found in individual crash categories. The third analysis dealt with the development of a logistic regression model to obtain the weather condition at a given time and location on I-4 in Central Florida so that this information can be used in traffic safety analyses, because of the lack of weather monitoring stations in the study area. To prove the worthiness of the weather information obtained form the analysis, the same weather information was used in a safety model developed by Abdel-Aty et al., 2004. It was also proved that the inclusion of weather information actually improved the safety model with better prediction accuracy.
M.S.C.E.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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Prasad, Jonathan P. "Zero-Inflated Censored Regression Models: An Application with Episode of Care Data." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2226.

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The objective of this project is to fit a sequence of increasingly complex zero-inflated censored regression models to a known data set. It is quite common to find censored count data in statistical analyses of health-related data. Modeling such data while ignoring the censoring, zero-inflation, and overdispersion often results in biased parameter estimates. This project develops various regression models that can be used to predict a count response variable that is affected by various predictor variables. The regression parameters are estimated with Bayesian analysis using a Markov chain Monte Carlo (MCMC) algorithm. The tests for model adequacy are discussed and the models are applied to an observed data set.
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Lindberg, Erik. "A study of the effect of inbreeding in Skellefteå during the 19th century : Using Cox Proportional hazard model to analyze lifespans and Poisson/Negative Binomial regression to analyze fertility." Thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-122687.

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Inbreeding is defined as when two individuals who are related mate and produce offspring. The level of inbreeding for an individual can be determined by calculating an inbreeding coefficient. Inbreeding can enhance both positive and negative traits. The risk for recessive diseases also increase. Data from old church records from the region of Skellefteå covering individuals from the late 17th century to the early 20th century has been made available. From this data parent-child relations can be observed and levels of inbreeding calculated. By analyzing the available data using Cox Proportional Hazard regression model it was shown that the level inbreeding affected the lifespan of an individual negatively if the parents are second cousins or more closely related. Using Poisson- and Negative Binomial regression, no evicence of an effect of inbreeding of fertility could be found.
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Vavilikolanu, Srutha. "Crash Prediction Models on Truck-Related Crashes on Two-lane Rural Highways with Vertical Curves." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1221758522.

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Darby, Phillip. "The assessment of driver and manager training in the context of work-related road safety interventions." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/20900.

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Vehicles being driven for work purposes represent a large proportion of road collision and deaths in the workplace. These observations mean that people driving for work can impose a large burden on organisations and on society. In addition, previous studies identified a fleet driver effect for which there was greater collision risk for those who drive for work compared to the general driving population, even after controlling for exposure. This accentuates the need for both organisational and government policy makers to take steps to reduce the impact of these collisions. No single intervention has been found to solve issues around work-related road safety therefore a range of initiatives have been directed towards the risks associated with drivers, vehicles, journeys and organisations. Many of the interventions, however, lack robust evidence to support their use. The aim of this thesis is to assess organisational interventions to improve work-related road safety by using econometric models on real-world data. The data represents driving claims made between 2005 and 2012 by employees of a large UK company, with a fleet of approximately 35,000 vehicles. The drivers were employed in a variety of roles such as working in technical positions at customer sites or making sales visits. The company has applied a range of strategies to road safety resulting in annual claim reductions of 7.7% compared to only a 4.5% reduction in collisions nationally. The company s data are used to undertake three studies which focused on driver training, manager training and claim segmentation. Statistical models were employed to investigate the effect of two different driver training courses on the frequency of claims while controlling for other factors. The results indicated that driver training courses significantly reduced both the total number of claims and the claim types targeted by the training. The impacts of the interventions were also adjusted for the effects of non-random driver selection and other safety improvements initiated by the company or other agencies. An important finding of this work was that randomly inflated pre-training events accounted for between a third and a quarter of the observed reduction in claims following training. The second study evaluated the impact of management training on claims using multilevel models which allowed for correlation between observations. The study could not confirm that this training was an effective safety intervention. This null result provides an incentive to re-evaluate the implementation of the scheme. The final study identified homogeneous claim segments using statistical models and the impact of training was evaluated on these segments. Such claims were estimated to be reduced by between 32% to 55% following existing driver training courses. This thesis has helped close important gaps and contributed to knowledge in terms of both intervention methodology and the understanding of the effectiveness of work-related road safety interventions. The results, which are already being applied in the case study organisation, demonstrated that training employees in either safe and fuel efficient driving, or low speed manoeuvring, reduced vehicle insurance claims. Further work is necessary to verify the safety value of manager training including gathering detailed information on interactions between managers and drivers.
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Coyle, Jesse Aaron. "Optimization of nuclear, radiological, biological, and chemical terrorism incidence models through the use of simulated annealing Monte Carlo and iterative methods." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43599.

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A random search optimization method based off an analogous process for the slow cooling of metals is explored and used to find the optimum solution for a number of regression models that analyze nuclear, radiological, biological,and chemical terrorism targets. A non-parametric simulation based off of historical data is also explored. Simulated series of 30 years and a 30 year extrapolation of historical data are provided. The inclusion of independent variables used in the regression analysis is based off existing work in the reviewed literature. CBRN terrorism data is collected from both the Monterey Institute's Weapons of Mass Destruction Terrorism Database as well as from the START Global Terrorism Database. Building similar models to those found in the literature and running them against CBRN terrorism incidence data determines if conventional terrorism indicator variables are also significant predictors of CBRN terrorism targets. The negative binomial model was determined to be the best regression model available for the data analysis. Two general types of models are developed, including an economic development model and a political risk model. From the economic development model we find that national GDP, GDP per capita, trade openness, and democracy to significant indicators of CBRN terrorism targets. Additionally from the political risk model we find corrupt, stable, and democratic regimes more likely to experience a CBRN event. We do not find language/religious fractionalization to be a significant predictive variable. Similarly we do not find ethnic tensions, involvement in external conflict, or a military government to have significant predictive value.
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Books on the topic "Negative binomial regression model"

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Negative binomial regression. 2nd ed. Cambridge, UK: Cambridge University Press, 2011.

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Greene, William. Functional Form and Heterogeneity in Models for Count Data. Now Publishers Inc, 2007.

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Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2007.

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Negative Binomial Regression. Cambridge University Press, 2007.

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Negative Binomial Regression. Cambridge, UK: Cambridge University Press, 2007.

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Negative Binomial Regression. Cambridfe University Press, 2007.

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Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2008.

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Aye, Goodness C. Wealth inequality and CO2 emissions in emerging economies: The case of BRICS. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/918-1.

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As the world battles with the triple problems of social, economic, and environmental challenges, it has become important to focus both policy and research efforts on these. Therefore, this study examines the effect of wealth inequality on CO2 emissions in five emerging economies: Brazil, Russia, India, China, and South Africa. The top decile of wealth share was used as a measure of wealth inequality, while CO2 emissions per capita were used as a measure of CO2 emissions. GDP per capita, population, and financial development (domestic credit to the private sector) were included as control variables. A balanced panel dataset of annual observations from 2000 to 2014 for these countries was used. Both fixed and random effects panel models were estimated, but the Hausman test favoured the use of the fixed effects model. The results based on the fixed effects panel regression model show that wealth inequality, GDP per capita, and population have positive effects on CO2 emissions, while financial development has a negative effect.
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Book chapters on the topic "Negative binomial regression model"

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Westfall, Peter H., and Andrea L. Arias. "Models for Poisson and Negative Binomial Response." In Understanding Regression Analysis, 361–77. Boca Raton : CRC Press, [2020]: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003025764-14.

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Reangsephet, Orawan, Supranee Lisawadi, and S. Ejaz Ahmed. "Improving Estimation of Regression Parameters in Negative Binomial Regression Model." In Proceedings of the Twelfth International Conference on Management Science and Engineering Management, 265–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93351-1_22.

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Gujarati, Damodar. "Modeling Count Data: The Poisson and Negative Binomial Regression Models." In Econometrics, 236–48. London: Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-37502-5_12.

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Cummings, Peter. "Negative Binomial Regression." In Analysis of Incidence Rates, 271–92. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429055713-17.

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Lemaire, Jean. "Introduction: The Negative Binomial Model." In Automobile Insurance, 117–27. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-015-7708-3_12.

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Asar, Yasin. "Liu-Type Negative Binomial Regression: A Comparison of Recent Estimators and Applications." In Contributions to Statistics, 23–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73241-1_2.

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Jing, Wei, Liu Haisheng, and Gui Wenyong. "Ruin Probability of Double Type Insurance Compound Negative Binomial Risk Model." In Communications in Computer and Information Science, 341–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34041-3_49.

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Rinott, Yosef, and Natalie Shlomo. "A Generalized Negative Binomial Smoothing Model for Sample Disclosure Risk Estimation." In Privacy in Statistical Databases, 82–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11930242_8.

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Raspa, G., R. Bruno, and P. Kokkiniotis. "An Application of Disjunctive Kriging: Using The Negative Binomial Model with Different Change of Support Models." In Geostatistics, 935–45. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9_74.

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Awazi, Nyong Princely, Martin Ngankam Tchamba, Lucie Felicite Temgoua, and Marie-Louise Tientcheu-Avana. "Farmers’ Adaptive Capacity to Climate Change in Africa: Small-Scale Farmers in Cameroon." In African Handbook of Climate Change Adaptation, 87–115. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_9.

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AbstractSmall-scale farmers’ limited adaptive capacity confronted with the adversities of climate change is a major call for concern considering that small-scale farms feed over half of the world’s population. In this light, small-scale farmers’ adaptive choices and adaptive capacity to climate change were assessed. Data were collected from primary and secondary sources using a mixed research approach. Findings revealed that extreme weather events have been recurrent and small-scale farmers perceived access to land, household income, and the planting of trees/shrubs on farms (agroforestry) as the main factors influencing their capacity to adapt to climate change. Agroforestry and monoculture practices were the main adaptive choices of small-scale farmers confronted with climate change. T-test and chi-square test statistics revealed a strong non-cause-effect relationship (p < 0.001) between small-scale farmers’ capacity to adapt to climate change and different socio-economic, institutional, and environmental variables. Parameter estimates of the binomial logistic regression model indicated the existence of a strong direct cause-effect relationship (p < 0.05) between small-scale farmers’ capacity to adapt to climate change and access to credit, household income, number of farms, access to information, and access to land, indicating that these variables enhance small-scale farmers’ capacity to adapt to climate change. It is recommended that policy makers examine the adaptive choices and determinants of farmers’ adaptive capacity unearthed in this chapter when formulating policies geared towards enhancing small-scale farmers’ capacity to adapt to climate change.
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Conference papers on the topic "Negative binomial regression model"

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Faroughi, Pouya, and Noriszura Ismail. "A new bivariate negative binomial regression model." In INTERNATIONAL CONFERENCE ON QUANTITATIVE SCIENCES AND ITS APPLICATIONS (ICOQSIA 2014): Proceedings of the 3rd International Conference on Quantitative Sciences and Its Applications. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4903663.

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Hassan, Anwar, Ishfaq S. Ahmad, and Peer Bilal Ahmad. "Non-Central Negative Binomial Regression Model for Count Data." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9197974.

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Shafira, Shafira, Sarini Abdullah, and Dian Lestari. "Bayesian Zero Inflated Negative Binomial Regression Model for The Parkinson Data." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290530.

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Jiang, Wei, Yutian Tang, Leiyu Ge, and Ming Li. "Maximum Likelihood Estimation - Negative Binomial Regression Traffic Conflict Prediction Model Considering Variable Interactivity." In 2019 5th International Conference on Transportation Information and Safety (ICTIS). IEEE, 2019. http://dx.doi.org/10.1109/ictis.2019.8883720.

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Fadilah, Febitri Wahyu Rizki, Sri Sulistijowati Handajani, Etik Zukhronah, and Hasih Pratiwi. "Geographically weighted negative binomial regression model to analysis of factors that influence on maternal mortality in Central Java Province." In INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ICSAS) 2019. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5141718.

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Adenomon, Monday Osagie, and Gbenga Solomon Akinyemi. "Statistical Analysis of Tuberculosis and HIV Cases in West Africa Using Panel Poisson and Negative Binomial Regression Models." In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS). IEEE, 2020. http://dx.doi.org/10.1109/icmcecs47690.2020.240843.

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Tohari, Amin, Nur Chamidah, and Fatmawati. "Estimating model of the number of HIV and AIDS cases in East Java using bi-response negative binomial regression based on local linear estimator." In SYMPOSIUM ON BIOMATHEMATICS 2019 (SYMOMATH 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0023451.

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Wang, Yanlei, Shuang Xu, and Xiang Liu. "Risk Analysis of Freight Train Collisions in the United States, 2000 to 2014." In 2016 Joint Rail Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/jrc2016-5738.

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Train accidents damage infrastructure and rolling stock, disrupt operations, and may result in casualties and environmental damage. While the majority of previous studies focused on the safety risks associated with train derailments or highway-rail grade crossing collisions, much less work has been undertaken to evaluate train collision risk. This paper develops a statistical risk analysis methodology for freight-train collisions in the United States between 2000 and 2014. Negative binomial regression models are developed to estimate the frequency of freight-train collisions as a function of year and traffic volume by accident cause. Train collision severity, measured by the average number of railcars derailed, varied with accident cause. Train collision risk, defined as the product of collision frequency and severity, is predicted for 2015 to 2017, based on the 2000 to 2014 safety trend. The statistical procedures developed in this paper can be adapted to various other types of consequences, such as damage costs or casualties. Ultimately, this paper and its sequent studies aim to provide the railroad industry with data analytic tools to discover useful information from historical accidents so as to make risk-informed safety decisions.
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Jing, Lv, Xie Shan, and Liu Zhifeng. "Compound Negative Binomial-Binomial Risk Model." In 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2014. http://dx.doi.org/10.1109/icmtma.2014.48.

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Yotenka, Rahmadi, and Alfazrin Banapon. "Modelling the Number of Tuberculosis (TB) Cases in Indonesia using Poisson Regression and Negative Binomial Regression." In The 2nd International Seminar on Science and Technology (ISSTEC 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.201010.007.

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Reports on the topic "Negative binomial regression model"

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Ukkusuri, Satish, Lu Ling, Tho V. Le, and Wenbo Zhang. Performance of Right-Turn Lane Designs at Intersections. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317277.

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Right-turn lane (RTL) crashes are among the most key contributors to intersection crashes in the US. Different right turn lanes based on their design, traffic volume, and location have varying levels of crash risk. Therefore, engineers and researchers have been looking for alternative ways to improve the safety and operations for right-turn traffic. This study investigates the traffic safety performance of the RTL in Indiana state based on multi-sources, including official crash reports, official database, and field study. To understand the RTL crashes' influencing factors, we introduce a random effect negative binomial model and log-linear model to estimate the impact of influencing factors on the crash frequency and severity and adopt the robustness test to verify the reliability of estimations. In addition to the environmental factors, spatial and temporal factors, intersection, and RTL geometric factors, we propose build environment factors such as the RTL geometrics and intersection characteristics to address the endogeneity issues, which is rarely addressed in the accident-related research literature. Last, we develop a case study with the help of the Indiana Department of Transportation (INDOT). The empirical analyses indicate that RTL crash frequency and severity is mainly influenced by turn radius, traffic control, and other intersection related factors such as right-turn type and speed limit, channelized type, and AADT, acceleration lane and AADT. In particular, the effects of these factors are different among counties and right turn lane roadway types.
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