Relevant bibliographies by topics / Negativ binomial

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Negativ binomial'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Negativ binomial"

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Wolfstein, Axel. "Erweiterungen des negativ-binomial-modells für bonus-/malus-systeme." Blätter der DGVFM 22, no. 4 (October 1996): 723–54. http://dx.doi.org/10.1007/bf02808405.

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Wolfstein, Axel. "Ergänzungen zu “Erweiterungen des Negativ-Binomial-Modells für Bonus/Malus-Systeme”." Blätter der DGVFM 23, no. 3 (April 1998): 285–92. http://dx.doi.org/10.1007/bf02808291.

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Teerapabolarn, K. "Negative binomial approximation for independent negative binomial variables." Applied Mathematical Sciences 9 (2015): 239–42. http://dx.doi.org/10.12988/ams.2015.411974.

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Teerapabolarn, K. "Pointwise negative binomial approximation for independent negative binomial variables." Applied Mathematical Sciences 9 (2015): 1633–36. http://dx.doi.org/10.12988/ams.2015.5151.

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McElduff, Fiona. "Negative Binomial Regression." Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, no. 3 (June 2008): 758–59. http://dx.doi.org/10.1111/j.1467-985x.2008.00538_6.x.

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Imoto, Tomoaki. "Convolution of Binomial and Negative Binomial Variables." Communications in Statistics - Theory and Methods 44, no. 23 (October 19, 2015): 5005–22. http://dx.doi.org/10.1080/03610926.2013.809110.

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Gantsog, T., A. Joshi, and R. Tanas. "Phase properties of binomial and negative binomial states." Quantum Optics: Journal of the European Optical Society Part B 6, no. 6 (December 1994): 517–26. http://dx.doi.org/10.1088/0954-8998/6/6/002.

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García-Pérez, Miguel A., and Vicente Núñez-Antón. "Statistical Inference Involving Binomial and Negative Binomial Parameters." Spanish journal of psychology 12, no. 1 (May 2009): 288–307. http://dx.doi.org/10.1017/s1138741600001694.

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Statistical inference about two binomial parameters implies that they are both estimated by binomial sampling. There are occasions in which one aims at testing the equality of two binomial parameters before and after the occurrence of the first success along a sequence of Bernoulli trials. In these cases, the binomial parameter before the first success is estimated by negative binomial sampling whereas that after the first success is estimated by binomial sampling, and both estimates are related. This paper derives statistical tools to test two hypotheses, namely, that both binomial parameters equal some specified value and that both parameters are equal though unknown. Simulation studies are used to show that in small samples both tests are accurate in keeping the nominal Type-I error rates, and also to determine sample size requirements to detect large, medium, and small effects with adequate power. Additional simulations also show that the tests are sufficiently robust to certain violations of their assumptions.
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Iso, C., and K. Mori. "Negative binomial multiplicity distribution from binomial cluster production." Zeitschrift für Physik C: Particles and Fields 46, no. 1 (March 1990): 59–61. http://dx.doi.org/10.1007/bf02440833.

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Vellaisamy, P., P. Vellaisamy, N. S. Upadhye, N. S. Upadhye, Vydas Cekanavicius, and Vydas Cekanavicius. "On negative binomial approximation." Teoriya Veroyatnostei i ee Primeneniya 57, no. 1 (2012): 141–54. http://dx.doi.org/10.4213/tvp4435.

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Dissertations / Theses on the topic "Negativ binomial"

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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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Brodersson, Anna Lilly. "Flygbesiktning av Luftledningar : Modellering av samband mellan besiktningsanmärkningar och systemtillförlitlighet." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219572.

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This paper thoroughly investigates needs and requirements for overhead distribution feeder inspection and develops models to investigate possible relations between short term inspections remarks and outages. The study was conducted in collaboration with Fortum Distribution AB that supplied extensive information about their overhead power feeders concerning both inspection and power outages. The investigated models where lognormal linear model, Poisson generalized linear model and negative binomial generalized linear model. All models were implemented utilizing offset terms to compensate for differences in feeder length and amount of overhead versus underground feeders. The Poisson generalized linear model was rejected at an early stage due to overdispersion and neither of the remaining models fit the data perfectly. There for conclusions were primarily concluded from similarities and differences amongst the models. The results either implicate that maintenance is scheduled sufficiently fast, with respect to short term inspection, to ensure a high system reliability or no relationships between short term inspection and feeder outages exist. Therefore the final conclusion was that maintenance and reinvestment decisions should not be based on short cycle inspection data.
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Liu, Luchen. "Interval Estimation for Binomial Proportion, Poisson Mean, and Negative –binomial Mean." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175767.

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This paper studies the interval estimation of three discrete distributions: thebinomial distribution, the Poisson distribution and the negative-binomialdistribution. The problem is the chaotic behavior of the coverage probabilityfor the Wald interval. To solve this problem, alternative confidence intervals areintroduced. Coverage probability and expected length are chosen to be thecriteria evaluating the intervals.In this paper, I firstly tested the chaotic behavior of the coverageprobability for the Wald interval, and introduced the alternative confidenceintervals. Then I calculated the coverage probability and expected length forthose intervals, made comparisons and recommended confidence intervals forthe three cases. This paper also discussed the relationship among the threediscrete distributions, and in the end illustrated the applications on binomialand Poisson data with brief examples.
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Oki, Fabio Hideto. "Modelos semiparamétricos com resposta binomial negativa." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-26082015-161139/.

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O objetivo principal deste trabalho é discutir estimação e diagnóstico em modelos semiparamétricos com resposta binomial negativa, mais especificamente, modelos de regressão com resposta binomial negativa em que uma das variáveis explicativas contínuas é modelada de forma não paramétrica. Iniciamos o trabalho com um exemplo ilustrativo e fazemos uma breve revisão dos modelos paramétricos com resposta binomial negativa. Em seguida, introduzimos os modelos semiparamétricos com resposta binomial negativa e discutimos alguns aspectos de estimação, inferência e seleção de modelos. Dedicamos um capítulo a procedimentos de diagnóstico, tais como desenvolvimento de medidas de alavanca e de influência sob os aspectos de deleção de pontos e influência local, além de abordar a análise de resíduos. Reanalizamos o exemplo ilustrativo sob o enfoque semiparamétrico e apresentamos algumas conclusões.
The aim of this work is to discuss some aspects on estimation and diagnostics in negative binomial regression models which an explanatory continuous variable is modeled nonparametrically. First, an illustrative example is presented and analyzed under parametric negative binomial regression models. The proposed models are then introduced and some aspects on estimations, inference and model selection are presented. Particular emphasis is given on the development of diagnostic procedures, such as leverage measures, Cook distances, local influence approach and residuals. The motivated example is reanalyzed under the semiparametric viewpoint and some conclusions are given.
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OLIVEIRA, Cícero Carlos Felix de. "Uma priori beta para distribuição binomial negativa." Universidade Federal Rural de Pernambuco, 2011. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4537.

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This dissertation is being dealt with a discrete distribution based on Bernoulli trials, which is the Negative Binomial distribution. The main objective is to propose a new non-informative prior distribution for the Negative Binomial model, which is being termed as a possible prior distribution Beta(0; 0), which is an improper distribution. This distribution is also known for the Binomial model as Haldane prior, but for the Negative Binomial model there are no studies to date. The study of the behavior of this prior was based on Bayesian and classical contexts. The idea of using a non-informative prior is the desire to make statistical inference based on the minimum of information prior subjective as possible. Well, makes it possible to compare the results of classical inference that uses only sample information, for example, the maximum likelihood estimator. When is compared the Beta(0; 0) distribution with the Bayes-Laplace prior and Jeffreys prior, based on the Bayesian estimators (posterior mean and posterior mode) and the maximum likelihood estimator, note that the possible Beta(0; 0) prior is less informative than the others prior. It is also verified that is prior possible is a limited distribution in parameter space, thus, an important feature for non-informative prior. The main argument shows that the possible Beta(0; 0) prior is adequate, when it is applied in a predictive posterior distribution for Negative Binomial model, leading the a Beta-Negative Binomial distribution (which corresponds the a hypergeometric multiplied by a probability). All observations citas are strengthened by several studies, such as: basic concepts related to Bayesian Inference and concepts of the negative binomial distribution and Beta-Negative Binomial (a mixture of Beta with the negative binomial) distribution.
Nesta dissertação está sendo abordado uma distribuição discreta baseada em ensaios de Bernoulli, que é a distribuição Binomial Negativa. O objetivo principal é prôpor uma nova distribuição a priori não informativa para o modelo Binomial Negativa, que está sendo denominado como uma possível distribuição a priori Beta(0; 0), que é uma distribuição imprópria. Essa distribuição também é conhecida para o modelo Binomial como a priori de Haldane, mas para o modelo Binomial Negativa não há nenhum estudo até o momento. O estudo do comportamento desta a priori foi baseada nos contextos bayesiano e clássico. A ideia da utilização de uma a priori não informativa é o desejo de fazer inferência estatística baseada no mínimo de informação subjetiva a priori quanto seja possível. Assim, torna possível a comparação com os resultados da inferência clássica que só usa informação amostral, como por exemplo, o estimador de máxima verossimilhança. Quando é comparado a distribuição Beta(0; 0) com a priori de Bayes - Laplace e a priori de Jeffreys, baseado-se nos estimadores bayesiano (média a posteriori e moda a posteriori) e no estimador de máxima verossimilhança, nota-se que a possível a priori Beta(0; 0) é menos informativa do que as outras a priori. É verificado também, que esta possível a priori é uma distribuição limitada no espaço paramétrico, sendo assim, uma característica importante para a priori não informativa. O principal argumento mostra que a possível a priori Beta(0; 0) é adequada, quando ela é aplicada numa distribuição a posteriori preditiva para modelo Binomial Negativa, levando a uma distribuição Beta Binomial Negativa (que corresponde a uma hipergeométrica multiplicada por uma probabilidade). Todas as observações citadas são fortalecidas por alguns estudos feitos, tais como: conceitos básicos associados à Inferência Bayesiana e conceitos das distribuições Binomial Negativa e Beta Binomial Negativa (que uma mistura da Beta com a Binomial Negativa).
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Szarka, John Louis III. "Surveillance of Negative Binomial and Bernoulli Processes." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26617.

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The evaluation of discrete processes are performed for industrial and healthcare processes. Count data may be used to measure the number of defective items in industrial applications or the incidence of a certain disease at a health facility. Another classification of a discrete random variable is for binary data, where information on an item can be classified as conforming or nonconforming in a manufacturing context, or a patient's status of having a disease in health-related applications. The first phase of this research uses discrete count data modeled from the Poisson and negative binomial distributions in a healthcare setting. Syndromic counts are currently monitored by the BioSense program within the Centers for Disease Control and Prevention (CDC) to provide real-time biosurveillance. The Early Aberration Reporting System (EARS) uses recent baseline information comparatively with a current day's syndromic count to determine if outbreaks may be present. An adaptive threshold method is proposed based on fitting baseline data to a parametric distribution, then calculating an upper-tailed p-value. These statistics are then converted to an approximately standard normal random variable. Monitoring is examined for independent and identically distributed data as well as data following several seasonal patterns. An exponentially weighted moving average (EWMA) chart is also used for these methods. The effectiveness of these methods in detecting simulated outbreaks in several sensitivity analyses is evaluated. The second phase of research explored in this dissertation considers information that can be classified as a binary event. In industry, it is desirable to have the probability of a nonconforming item, p, be extremely small. Traditional Shewhart charts such as the p-chart, are not reliable for monitoring this type of process. A comprehensive literature review of control chart procedures for this type of process is given. The equivalence between two cumulative sum (CUSUM) charts, based on geometric and Bernoulli random variables is explored. An evaluation of the unit and group--runs (UGR) chart is performed, where it is shown that the in--control behavior of this chart is quite misleading and should not be recommended for practitioners.
Ph. D.
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Toledo, Francisco Ricardo de [UNESP]. "Distribuição espacial e amostragem de adultos de Toxoptera citricida Kirkaldy (Hemiptera: Aphididae) na cultura de citros." Universidade Estadual Paulista (UNESP), 2006. http://hdl.handle.net/11449/96984.

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O estudo da distribuição espacial de pragas é fundamental para elaboração de planos de amostragem para uso no manejo integrado de pragas. Para Toxoptera citricida foi estudada a distribuição espacial em talhões de pomares de citros comerciais de laranja (Citrus sinensis) da variedade Pêra, com 5, 9 e 15 anos de idade, durante o período de setembro de 2004 a abril de 2005. Foram realizadas 14 amostragens de número de pulgões-preto em intervalos aproximados de 15 dias entre as mesmas, utilizando-se armadilhas adesivas de cor amarela (0,11 x 0,11 m) fixadas à planta a 1,5 m de altura aproximadamente. As armadilhas foram distribuídas na área, a cada cinco plantas na linha, em linhas alternadas, totalizando 137 armadilhas no talhão com 5 anos, 140 no talhão com 9 anos e 80 no talhão com 15 anos. A lei de Taylor e a distribuição binomial negativa foram os modelos que melhor representaram a distribuição da população. Foram com construídos planos de amostragens para levantamento desta praga com base na lei de Taylor e na distribuição binomial negativa.
The study of spatial distribution of insects is fundamental to elaborate sampling plans with potential to use in integrated pest management. The spatial distribution of Toxoptera citricida was studied in plots of commercial orchards of orange (Citrus sinensis) of the variety 'Pêra' with 5, 9 and 15 years of age, during the period of September of 2004 and April of 2005. Fourteen samples of the number of Toxoptera citricida was performed each 15d approximately, using yellow adhesive traps fixed at 1,5 m of height each 5 plants in alternated lines, summing 137 traps in the 5-years plot, 140 traps in the 9-years plot and 80 traps in the 15-years plot. The best models fitted the distribution of population were the Taylor Law and negative binomial distribution, which were used to elaborate the sampling plans.
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Oliveira, Tiago Carvalhais de. "Flutuação populacional de lagartas desfolhadoras e distribuição espacial de Plusiinae na cultura da soja [Glycine Max (L.) Merril]." Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4451.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Caterpillars defoliators of soybean occurs in different times and feed on different parts of the plant, mainly in flowers and pods. During the growing seasons of 2011/2012 and 2012/2013, soybean fields were sampled in different crop phenological stages with the objective of knowing the population fluctuation of the main species of caterpillar defoliating soybeans and to determine the within field spatial distribution of the species of the Plusiinae complex, to describe how species colonize the soybean field. Five commercial fields submitted to different management practices were monitored. Sampling meshes were fabricated with equidistant points 50 meters. The areas were sampled weekly from the occurrence of the first defoliating caterpillars, until close to harvest. The sampling method used was ground cloth, with one row-meter. It was quantified all caterpillars. Data were submitted to statistical analysis using the following SAS procedures: Proc Univariate, Proc Means, Proc Freq. To test what statistical distribution fit to caterpillars population the procedure Proc Genmode was used. Maps representing the localization of samples of caterpillars in soybean fields were constructed. The species of Plusiinae predominated in the area, corresponding to 52, 04% in 2011/2012 e 48, 53% in 2012/2013 of the total of caterpillars found in the reproductive phase of soybean. The distribution of the complex of Plusiinae within field is random and do not present border effect. In this study, while the population fits the negative binomial distribution maps clearly indicate that the caterpillars are randomly dispersed in soybean fields.
As lagartas desfolhadoras da soja ocorrem em épocas distintas e atacam várias partes da planta, principalmente as folhas, flores e frutos. Durante os anos agrícolas de 2011/2012 e 2012/2013, campos de soja foram amostrados em diversos estádios fenológicos da planta com o objetivo de conhecer a flutuação populacional das principais espécies de lagartas desfolhadoras da cultura, e determinar a distribuição espacial em lavoura, espécies pertencentes ao complexo de Plusiinae, para compreender a forma que este complexo coloniza os campos de soja. Foram monitoradas cinco lavouras comerciais, submetidas a diferentes condições de manejo. Foram confeccionadas malhas de amostragem, com pontos eqüidistantes de 50 metros. As áreas foram amostradas semanalmente a partir da ocorrência das primeiras lagartas desfolhadoras, até próximo à colheita. O método de amostragem utilizado foi o pano de batida, com o qual um metro linear foi amostrado. Foram quantificadas todas as lagartas presentes nas amostragens dos campos selecionados. Para a realização das inferências, dos dados foram submetidos á análises estatísticas descritivas utilizando os seguintes procedimentos dos Sas, Proc Univariate, Proc Means e Proc Freq. Para verificar qual a distribuição estatística a que a população de lagartas melhor se ajustava foi utilizado o procedimento Proc Genmode. Mapas representativos da localização das lagartas do complexo Plusiinae no campo foram confeccionados. As espécies de Plusiinae foram as lagartas predominantes, compondo 52,04% em 2011/12 e 48,53% em 2012/13, do total de lagartas encontradas. As flutuações populacionais demonstraram que o pico das lagartas desfolhadoras ocorre principalmente na fase reprodutiva da cultura da soja. A distribuição de lagartas do complexo Plusiinae em lavoura é aleatória, não apresentando efeitos de bordas. Neste estudo, enquanto a população se ajusta à distribuição binomial negativa os mapas indicam claramente que as lagartas estão aleatoriamente dispersas nos campos de soja.
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El-Berry, S. E. M. "Some geometric and negative binomial time series models." Thesis, University of Strathclyde, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381119.

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Rodrigues, Thais Carvalho Valadares. "Regressão binomial negativa geograficamente ponderada : modelando superdispersão espacial." reponame:Repositório Institucional da UnB, 2012. http://repositorio.unb.br/handle/10482/10348.

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Dissertação (mestrado)—Universidade de Brasília, Departamento de Estatística, 2012.
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A regressão global pressupõe que um modelo único é adequado para descrever todas as partes de uma região de estudo. No entanto, a força dos relacionamentos entre as variáveis pode não ser espacialmente constante. Além disso, os fatores envolvidos são geralmente tão complexos, que é difícil identificá-los na forma de variáveis explanatórias. Muitas vezes, ainda tem-se o problema de tamanho de amostra reduzido. Neste contexto, surge a Regressão Geograficamente Ponderada (RGP), a fim de modelar dados espaciais não est acionários. Utilizando funções kernel, a RGP permite que os parâmetros do modelo variem espacialmente, produzindo superfícies não paramétricas das suas estimativas. Considerando dados de contagem com superdispersão, o mais adequado é utilizar a distribuição Binomial Negativa. Por isso, o presente trabalho propõe dois modelos de Regressão Geograficamente Ponderada utilizando esta distribuição, a saber, RBNGPg e RBNGP. Estes modelos diferem-se na forma de estimação do parâmetro de superdispersão e, consequentemente, em termos de complexidade. Neste trabalho, os modelos propostos sao aplicados a 5 estudos de caso, envolvendo dados reais e simulados. Os resultados obtidos mostram a superioridade deles no ajuste de dados de contagem nao estacionários e com superdispersão com respeito aos modelos concorrentes, a saber, regressão global - Poisson e Binomial Negativa - e Regressão de Poisson Geograficamente Ponderada- Além disso, verifica-se que estes modelos concorrentes sao casos especiais do modelo mais robusto RBNGP. ______________________________________________________________________________ ABSTRACT
The global regression assumes that a single model is adequate to describe all parts of a study region. However, the strength of relationships between variables may not be spatially constant. In addition, the factors involved are often so complex that it is difficult to identify them in the form of explanatory variables. Many times, we also have the problem of small sample size. hi this context Geographically Weighted Regression (GW R) is introduced in order to model non-stationary spatial data. Using kernel functions, GW R allows the model parameters to vary spatially, producing non-parametric surfaces of their estimates. To model count data with overdispersion, the most- appropriate is to use the Negative Binomial distribution. Therefore, we propose two models of Geographically Weighted Regression using this distribution, namely GW NBRg and GW NBR. These models differ in the way the overdispersion parameter is estimated and, consequently, in terms of complexity. In this dissertation, the proposed models are applied to 5 case studies involving real and simulated data. The results show their superiority in modelling non-stationary count data with overdispersion with respect to competing models, namely, global regression - Poisson and Negative Binomial - and Geographically Weighted Poisson Regression. Moreover, we demonstrate that these competing models are special cases of the more robust model GW NBR.
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Books on the topic "Negativ binomial"

1

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

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Adejumo, Adebowale Olusola. Modelling generalized linear (loglinear) models for raters agreement measure: With complete and missing values cases. New York: Peter Lang, 2006.

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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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Modelling Generalized Linear-loglinear Models for Raters Agreement Measure: With Complete And Missing Values Cases (Anwendungsorientierte Statistik). Peter Lang Publishing, 2005.

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Modelling Generalized Linear-loglinear Models for Raters Agreement Measure: With Complete And Missing Values Cases (Anwendungsorientierte Statistik, Bd. 9). Peter Lang Publishing, 2005.

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

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Book chapters on the topic "Negativ binomial"

1

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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Nguyen, Hung T., and Gerald S. Rogers. "The Negative Binomial Distribution." In Springer Texts in Statistics, 177–83. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-1013-9_21.

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Bondesson, Lennart. "Generalized Negative Binomial Convolutions." In Generalized Gamma Convolutions and Related Classes of Distributions and Densities, 126–36. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2948-3_8.

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Kemp, Adrienne W. "Geometric and Negative Binomial Distributions." In International Encyclopedia of Statistical Science, 605–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_278.

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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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Joshi, Amitabh, and S. V. Lawande. "Properties of Squeezed Binomial States and Squeezed Negative Binomial States." In Recent Developments in Quantum Optics, 257–64. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2936-1_33.

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Gantsog, Ts, Amitabh Joshi, and R. Tanaś. "On the Phase Properties of Binomial and Negative Binomial States." In Coherence and Quantum Optics VII, 595–96. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9742-8_169.

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Islam, M. Ataharul, and Rafiqul I. Chowdhury. "Bivariate Negative Binomial and Multinomial Models." In Analysis of Repeated Measures Data, 125–38. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3794-8_9.

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Formichella, Sam, and Armin Straub. "Gaussian Binomial Coefficients with Negative Arguments." In Trends in Mathematics, 371–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-57050-7_22.

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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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Conference papers on the topic "Negativ binomial"

1

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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Guo, Dongning. "On information-Estimation relationships over binomial and negative binomial models." In 2013 IEEE International Symposium on Information Theory (ISIT). IEEE, 2013. http://dx.doi.org/10.1109/isit.2013.6620268.

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Jordanova, Pavlina K., Monika P. Petkova, and Milan Stehlík. "Compound negative binomial distribution with negative multinomial summands." In APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS (AMEE’16): Proceedings of the 42nd International Conference on Applications of Mathematics in Engineering and Economics. Author(s), 2016. http://dx.doi.org/10.1063/1.4968501.

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Taborda, Camilo G., Fernando Perez-Cruz, and Dongning Guo. "New information-estimation results for poisson, binomial and negative binomial models." In 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6875225.

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D'Souza, Richard, and Adya P. Mishra. "Generalized distribution of negative binomial states." In 1992 Shanghai International Symposium on Quantum Optics, edited by Yuzhu Wang, Yiqiu Wang, and Zugeng Wang. SPIE, 1992. http://dx.doi.org/10.1117/12.130401.

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Bodhisuwan, Winai, Chookait Pudprommarat, Rujira Bodhisuwan, and Luckhana Saothayanun. "Zero-truncated negative binomial - Erlang distribution." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012230.

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Taborda, Camilo G., and Fernando Perez-Cruz. "Mutual information and relative entropy over the Binomial and Negative Binomial channels." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6284304.

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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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SÖDERHOLM, JONAS, and SHUICHIRO INOUE. "THE NEGATIVE BINOMIAL DISTRIBUTION IN QUANTUM PHYSICS." In Proceedings of the 9th International Symposium. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814282130_0077.

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Zhang, Dapeng, and Xiaokun (Cara) Wang. "Household Travel Analysis Using Bayesian Negative Binomial Models." In The Twelfth COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412442.367.

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Reports on the topic "Negativ binomial"

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Gupta, Shanti S., and TaChen Liang. On Empirical Bayes Selection Rules for Negative Binomial Populations. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada196994.

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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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