Relevant bibliographies by topics / Binomial distribution

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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 'Binomial distribution'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Binomial distribution"

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Sampath, S. "Hybrid Binomial Distribution." International Journal of Fuzzy System Applications 2, no. 4 (October 2012): 64–75. http://dx.doi.org/10.4018/ijfsa.2012100104.

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Buckley and Eslami (2003) introduced a discrete distribution, namely Fuzzy Binomial distribution, that is intended to suit situations wherein impreciseness and randomness coexist. Using the approach of Liu (2008), a hybrid (combination of imprecision and randomness) version of Binomial distribution is developed. With the help of genetic algorithms the process of computing the chance distributions, expectation and variance of the distribution that is developed in this paper are illustrated. Illustrative examples are given to justify the usefulness of the distribution.
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Kalev, Krasimir. "APPLICATION OF BINOMIAL DISTRIBUTION IN LOGISTICS SYSTEMS." Journal Scientific and Applied Research 6, no. 1 (November 12, 2014): 114–20. http://dx.doi.org/10.46687/jsar.v6i1.147.

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A downtime interval during which a machine is performing no work due to lack of a spare part is an important economical issue for companies. It has to know in advance the need for spare elements to ensure reliable operation of the machines. In order to determine the required amount of spare elements used a scientific approach. In this paper is proposed a well-known statistical approach to inventory management. The binomial distribution permits to analyze without difficulties the operational reliability and calculating the spare parts demand. Some of results are given by engineering software.
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Čekanavičius, V., and B. Roos. "Binomial Approximation to the Markov Binomial Distribution." Acta Applicandae Mathematicae 96, no. 1-3 (March 23, 2007): 137–46. http://dx.doi.org/10.1007/s10440-007-9114-1.

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Ehm, Werner. "Binomial approximation to the Poisson binomial distribution." Statistics & Probability Letters 11, no. 1 (January 1991): 7–16. http://dx.doi.org/10.1016/0167-7152(91)90170-v.

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Handayani, Deby. "Karakterisasi Sebaran Binomial Negatif-Binomial Negatif." Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta 1, no. 2 (July 26, 2022): 94–97. http://dx.doi.org/10.47233/jppie.v1i2.558.

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This study discusses the convolution or the sum of independent and identical random variables, where the random variables are two distribution of Negative Binomial distribution so that the resulting distribution is known as the Negative Binomial - Negative Binomial. The purpose of this study is to find the characteristics of the distribution including the expected value, the variance value, the moment generating function and the characteristic function. This property is obtained by using theorems and lemmas that relate to the properties of a distribution. It is found that the expected value, variance value, moment generating function and characteristic function of the Negative Binomial-Exponential distribution are
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Anjela, Wanjala, and George Muhua. "Negative Binomial Three Parameter Lindley Distribution and Its Properties." International Journal of Theoretical and Applied Mathematics 10, no. 1 (June 14, 2024): 1–5. http://dx.doi.org/10.11648/j.ijtam.20241001.11.

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Many researchers have proposed mixed distributions as one of the most important methods for obtaining new probability distributions. Several studies have shown that mixed Negative Binomial distributions fits count data better than Poisson and Negative Binomial distribution itself. In this paper, we introduce a mixed distribution by mixing the distributions of negative binomial and three Parameter Lindley distribution. This new distribution has a thick tail and may be considered as an alternative for fitting count data with over dispersion. The parameters of the new distribution are estimated using MLE method and properties studied. Special cases of the new distribution and also identified. A simulation study carried out shows that the ML estimators give the parameter estimates close to the parameter when the sample is large, that is, the bias and variance of the parameter estimates decrease with increase in sample size showing the consistent nature of the new compound distribution. The study also compares the performance of the new distribution over distributions of Poisson, Negative Binomial, Negative Binomial oneParameter Lindley Distribution, Negative Binomial two Parameter distribution, three parameter Lindley distribution using a real count over dispersed dataset and the results shows that Negative Binomial three parameter Lindley distribution gave the smallest Kolmogorov Smirnov test statistic, AIC and BIC as compared to other distributions, hence the new distribution provided a better fit compared to other distributions under study for fitting over dispersed count data.
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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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MUNTEANU, Bogdan-Gheorghe. "QUALITATIVE ASPECTS OF THE MIN PARETO BINOMIAL DISTRIBUTION." Review of the Air Force Academy 15, no. 2 (October 20, 2017): 63–68. http://dx.doi.org/10.19062/1842-9238.2017.15.2.8.

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Ross, G. J. S., and D. A. Preece. "The Negative Binomial Distribution." Statistician 34, no. 3 (1985): 323. http://dx.doi.org/10.2307/2987659.

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Omey, E., J. Santos, and Gulck Van. "A Markov-binomial distribution." Applicable Analysis and Discrete Mathematics 2, no. 1 (2008): 38–50. http://dx.doi.org/10.2298/aadm0801038o.

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Dissertations / Theses on the topic "Binomial distribution"

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Hansen, Peder. "Approximating the Binomial Distribution by the Normal Distribution – Error and Accuracy." Thesis, Uppsala universitet, Matematisk statistik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155336.

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Wei, Jiajin. "Estimation of the reciprocal of a binomial proportion." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/847.

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As a classic parameter originated from the binomial distribution, the binomial pro- portion has been well studied in the literature due to its wide range of applications. In contrast, the reciprocal of the binomial proportion, also known as the inverse proportion, is often overlooked, although it plays an important role in sampling designs and clinical studies. To estimate the inverse proportion, a simple method is to apply the maximum likelihood estimation (MLE). This estimator is, however, not a valid estimator because it suffers from the zero-event problem, which occurs when there is no successful event in the trials. At first, we review a number of methods proposed to overcome the zero-event problem and discuss whether they are feasible to estimate the inverse proportion. Inspired by the Wilson (1927) and Agresti and Coull (1998), in this thesis, we focus on a family of shrinkage estimators of the inverse proportion and propose to derive the optimal estimator within this family. The shrinkage estimator overcomes the zero-event problem by including a positive shrinkage parameter, which is intrinsically related to the expected value of the resulting estimator. To find the best shrinkage parameter, the relationship between the shrinkage parameter and the estimation bias of the shrinkage estimator is investigated systematically. Note that the explicit expression of the expected value function of the estimator and the best shrinkage parameter are quite complicated to compute when the number of trials is large. Hence, we review three methods in the literature which were proposed to approximate the expected value function. And after being inspired, we propose a new approximate formula for the expected value function and derive an approximate solution of the optimal shrinkage parameter by the Taylor expansion. Because there still exist an unknown binomial proportion in the optimal shrinkage parameter, we suggest a plug-in estimator for the unknown proportion with an adaptive threshold. Finally, simulation studies are conducted to evaluate the performance of our new estimator. As baselines for comparison, we also include the Fattorini estimator, the Haldane estimator and a piecewise estimator in the simulations. According to the simulation results, the new estimator is able to achieve a better or equally good performance compared with the Fattorini estimators in most settings. Hence, our new estimator can be a reliable estimator for the inverse proportion in most practical cases
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ROCHA, Samy Marques. "Distribuição Binomial e Aplicações." Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1271.

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The Binomial probability distribution is one of the most commonly used to represent data of discrete random variables. In this work, we present the construction of the Binomial model and its main characteristics. The relationship with other distributions is explored following the theoretical aspects and examples of applications. The examples using data from the Brazilian soccer championship can become a motivational proposal for the students of the High School. The methodology is applied with the computational support of free software Geogebra.
A distribuição de probabilidade Binomial é uma das mais utilizadas para representar dados de variáveis aleatórias discretas. Neste trabalho, apresentamos a construção do modelo Binomial e suas principais caracter´ısticas. O relacionamento com outras distribuições é explorado seguindo os aspectos teóricos e exemplos de aplicações. Os exemplos usando dados do campeonato brasileiro de futebol podem se tornar uma proposta motivadora para os alunos do Ensino Médio. A metodologia é aplicada com o apoio computacional do software livre GeoGebra
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Dai, Xiaogang. "Score Test and Likelihood Ratio Test for Zero-Inflated Binomial Distribution and Geometric Distribution." TopSCHOLAR®, 2018. https://digitalcommons.wku.edu/theses/2447.

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The main purpose of this thesis is to compare the performance of the score test and the likelihood ratio test by computing type I errors and type II errors when the tests are applied to the geometric distribution and inflated binomial distribution. We first derive test statistics of the score test and the likelihood ratio test for both distributions. We then use the software package R to perform a simulation to study the behavior of the two tests. We derive the R codes to calculate the two types of error for each distribution. We create lots of samples to approximate the likelihood of type I error and type II error by changing the values of parameters. In the first chapter, we discuss the motivation behind the work presented in this thesis. Also, we introduce the definitions used throughout the paper. In the second chapter, we derive test statistics for the likelihood ratio test and the score test for the geometric distribution. For the score test, we consider the score test using both the observed information matrix and the expected information matrix, and obtain the score test statistic zO and zI . Chapter 3 discusses the likelihood ratio test and the score test for the inflated binomial distribution. The main parameter of interest is w, so p is a nuisance parameter in this case. We derive the likelihood ratio test statistics and the score test statistics to test w. In both tests, the nuisance parameter p is estimated using maximum likelihood estimator pˆ. We also consider the score test using both the observed and the expected information matrices. Chapter 4 focuses on the score test in the inflated binomial distribution. We generate data to follow the zero inflated binomial distribution by using the package R. We plot the graph of the ratio of the two score test statistics for the sample data, zI /zO , in terms of different values of n0, the number of zero values in the sample. In chapter 5, we discuss and compare the use of the score test using two types of information matrices. We perform a simulation study to estimate the two types of errors when applying the test to the geometric distribution and the inflated binomial distribution. We plot the percentage of the two errors by fixing different parameters, such as the probability p and the number of trials m. Finally, we conclude by briefly summarizing the results in chapter 6.
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Hörmann, Wolfgang. "The generation of binomial random variates." Institut für Statistik und Mathematik, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 1992. http://epub.wu.ac.at/1242/1/document.pdf.

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The transformed rejection method, a combination of inversion and rejection, which can be applied to various continuous distributions, is well suited to generate binomial random variates as well. The resulting algorithms are simple and fast, and need only a short set-up. Among the many possible variants two algorithms are described and tested: BTRS a short but nevertheless fast rejection algorithm and BTRD which is more complicated as the idea of decomposition is utilized. For BTRD the average number of uniforms required to return one binomial deviate lies between 2.5 and 1.4 which is considerably lower than for any of the known uniformly fast algorithms. Timings for a C-implementation show that for the case that the parameters of the binomial distribution vary from call to call BTRD is faster than the current state of the art algorithms. Depending on the computer, the speed of the uniform generator used and the binomial parameters the savings are between 5 and 40 percent. (author's abstract)
Series: Preprint Series / Department of Applied Statistics and Data Processing
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Rodrigues, Cristiane. "Distribuições em série de potências modificadas inflacionadas e distribuição Weibull binominal negativa." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-28062011-095106/.

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Neste trabalho, alguns resultados, tais como, função geradora de momentos, relações de recorrência para os momentos e alguns teoremas da classe de distribuições em séries de potencias modificadas (MPSD) proposta por Gupta (1974) e da classe de distribuições em séries de potências modificadas inflacionadas (IMPSD) tanto em um ponto diferente de zero como no ponto zero são apresentados. Uma aplicação do Modelo Poisson padrão, do modelo binomial negativo padrão e dos modelos inflacionados de zeros para dados de contagem, ZIP e ZINB, utilizando-se as técnicas dos MLGs, foi realizada para dois conjuntos de dados reais juntamente com o gráfico normal de probabilidade com envelopes simulados. Também foi proposta a distribuição Weibull binomial negativa (WNB) que é bastante flexível em análise de dados positivos e foram estudadas algumas de suas propriedades matemáticas. Esta é uma importante alternativa para os modelos Weibull e Weibull geométrica, sub-modelos da WNB. A demostração de que a densidade da distribuição Weibull binomial negativa pode ser expressa como uma mistura de densidades Weibull é apresentada. Fornecem-se, também, seus momentos, função geradora de momentos, gráficos da assimetria e curtose, expressoes expl´citas para os desvios médios, curvas de Bonferroni e Lorenz, função quantílica, confiabilidade e entropia, a densidade da estat´stica de ordem e expressões explícita para os momentos da estatística de ordem. O método de máxima verossimilhança é usado para estimar os parametros do modelo. A matriz de informação esperada ´e derivada. A utilidade da distribuição WNB está ilustrada na an´alise de dois conjuntos de dados reais.
In this paper, some result such as moments generating function, recurrence relations for moments and some theorems of the class of modified power series distributions (MPSD) proposed by Gupta (1974) and of the class of inflated modified power series distributions (IMPSD) both at a different point of zero as the zero point are presented. The standard Poisson model, the standard negative binomial model and zero inflated models for count data, ZIP and ZINB, using the techniques of the GLMs, were used to analyse two real data sets together with the normal plot with simulated envelopes. The new distribution Weibull negative binomial (WNB) was proposed. Some mathematical properties of the WNB distribution which is quite flexible in analyzing positive data were studied. It is an important alternative model to the Weibull, and Weibull geometric distributions as they are sub-models of WNB. We demonstrate that the WNB density can be expressed as a mixture of Weibull densities. We provide their moments, moment generating function, plots of the skewness and kurtosis, explicit expressions for the mean deviations, Bonferroni and Lorenz curves, quantile function, reliability and entropy, the density of order statistics and explicit expressions for the moments of order statistics. The method of maximum likelihood is used for estimating the model parameters. The expected information matrix is derived. The usefulness of the new distribution is illustrated in two analysis of real data sets.
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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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Luo, Shihua. "Bayesian Estimation of Small Proportions Using Binomial Group Test." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/744.

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Group testing has long been considered as a safe and sensible relative to one-at-a-time testing in applications where the prevalence rate p is small. In this thesis, we applied Bayes approach to estimate p using Beta-type prior distribution. First, we showed two Bayes estimators of p from prior on p derived from two different loss functions. Second, we presented two more Bayes estimators of p from prior on π according to two loss functions. We also displayed credible and HPD interval for p. In addition, we did intensive numerical studies. All results showed that the Bayes estimator was preferred over the usual maximum likelihood estimator (MLE) for small p. We also presented the optimal β for different p, m, and k.
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Philippsen, Adriana Strieder. "Abordagem clássica e bayesiana para os modelos de séries temporais da família GARMA com aplicações para dados de contagem." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-06062011-164536/.

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Nesta dissertação estudou-se o modelo GARMA para modelar séries temporais de dados de contagem com as distribuições condicionais de Poisson, binomial e binomial negativa. A principal finalidade foi analisar no contexto clássico e bayesiano, o desempenho e a qualidade do ajuste dos modelos de interesse, bem como o desempenho dos percentis de cobertura dos intervalos de confiança dos parâmetros para os modelos adotados. Para atingir tal finalidade considerou-se a análise dos estimadores pontuais bayesianos e foram analisados intervalos de credibilidade. Neste estudo é proposta uma distribuição a priori conjugada para os parâmetros dos modelos e busca-se a distribuição a posteriori, a qual associada a certas funções de perda permite encontrar estimativas bayesianas para os parâmetros. Na abordagem clássica foram calculados estimadores de máxima verossimilhança, usandose o método de score de Fisher e verificou-se por meio de simulação a consistência dos mesmos. Com os estudos desenvolvidos pode-se observar que, tanto a inferência clássica quanto a inferência bayesiana para os parâmetros dos modelos em questão, apresentou boas propriedades analisadas por meio das propriedades dos estimadores pontuais. A última etapa do trabalho consiste na análise de um conjunto de dados reais, sendo uma série real correspondente ao número de internações por causa da dengue em Campina Grande. Estes resultados mostram que tanto o estudo clássico, quanto o bayesiano, são capazes de descrever bem o comportamento da série
In this work, it was studied the GARMA model to model time series count data with Poisson, binomial and negative binomial discrete conditional distributions. The main goal is to analyze, in the bayesian and classic context, the performance and the quality of fit of the corresponding models, as well as the coverage percentages performance to these models. To achieve this purpose we considered the analysis of Bayesian estimators and credible intervals were analyzed. To the Bayesian study it was proposed a priori distribution joined to the models parameters and sought a posteriori distribution, which one associate with to certain loss functions allows finding out Bayesian estimates to the parameters. In the classical approach, it was calculated the maximum likelihood estimators using the method of Fisher scoring, whose interest was to verify, by simulation, the consistence. With the studies developed we can notice that, both classical and inference Bayesian inference for the parameters of those models, presented good properties analysed through the properties of the punctual estimators. The last stage of the work consisted of the analysis of one real data set, being a real serie corresponding to the admission number because of dengue in the city of Campina Grande. These results show that both the classic and the Bayesian studies are able to describe well the behavior of the serie
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Tian, Suzhong. "Statistical Inference for the Risk Ratio in 2x2 Binomial Trials with Stuctural Zero." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/TianS2004.pdf.

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Books on the topic "Binomial distribution"

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von Collani, Elart, and Klaus Dräger. Binomial Distribution Handbook for Scientists and Engineers. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0215-8.

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Leemis, Lawrence M. A comparison of approximate interval estimators for the Bernoulli parameter. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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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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Sinclair, Margaret. How to get an A in-- statistics & data analysis: Mean, median & mode, standard deviation, binomial distribution. Toronto: Coles Pub., 1998.

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Consortium for Mathematics and Its Applications (U.S.), Chedd-Angier Production Company, American Statistical Association, and Annenberg Media, eds. Against all odds--inside statistics: Disc 3, programs 9-12. S. Burlington, VT: Annenberg Media, 2011.

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Daniel, Oladele, and Harry Peach. Probability, Normal and Binomial Distribution. Independently Published, 2019.

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

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

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Book chapters on the topic "Binomial distribution"

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Laudański, Ludomir M. "Binomial Distribution." In Intelligent Systems Reference Library, 245–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25697-4_13.

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Laudański, Ludomir M. "Binomial Distribution." In Intelligent Systems Reference Library, 87–127. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25697-4_7.

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Kaliski, Burt. "Binomial Distribution." In Encyclopedia of Cryptography and Security, 86. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_396.

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Philippou, Andreas N., and Demetrios L. Antzoulakos. "Binomial Distribution." In International Encyclopedia of Statistical Science, 152–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_146.

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Gooch, Jan W. "Binomial Distribution." In Encyclopedic Dictionary of Polymers, 971. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15164.

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Lesigne, Emmanuel. "The binomial distribution." In The Student Mathematical Library, 15–17. Providence, Rhode Island: American Mathematical Society, 2005. http://dx.doi.org/10.1090/stml/028/05.

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Nguyen, Hung T., and Gerald S. Rogers. "The Binomial Distribution." In Springer Texts in Statistics, 123–39. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-1013-9_17.

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Jolicoeur, Pierre. "The binomial distribution." In Introduction to Biometry, 108–23. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4777-8_18.

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Russell, Kenneth G. "The Binomial Distribution." In Design of Experiments for Generalized Linear Models, 89–148. Boca Raton, Florida : CRC Press, [2019] | Series: Chapman & Hall/CRC interdisciplinary statistics: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429057489-4.

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Čekanavičius, V., and S. Y. Novak. "Markov Binomial distribution." In Compound Poisson Approximation, 150–67. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003478164-9.

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Conference papers on the topic "Binomial distribution"

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Fernández, Nicolás, Jaime García-García, Elizabeth Arredondo, and Isaac Imilpán. "Knowledge of Binomial Distribution in Pre-service Mathematics Teachers." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t8b2.

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The binomial distribution is one of the most important discrete distributions in probability and statistics; however, research identifies weaknesses in teachers’ and students’ application of binomial distributions for solving tasks beyond the direct use of the formula. Based on historical epistemological study and notions from the onto-semiotic approach to mathematical knowledge and instruction, we designed and administered a questionnaire to secondary school mathematics teachers in training. In our results, we identify and describe a lack of articulation among historical epistemological elements of the binomial distribution. Teachers can correctly use concepts such as combinatorics, probability, and the binomial distribution formula to model and identify binomial phenomena but cannot answer questions about a random variable or expected value. They show a lack of consideration of alternative representations.
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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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Denthet, Sunthree, Ampai Thongteeraparp, and Winai Bodhisuwan. "Mixed distribution of negative binomial and two-parameter Lindley distributions." In 2016 12th International Conference on Mathematics, Statistics, and Their Application (ICMSA). IEEE, 2016. http://dx.doi.org/10.1109/icmsa.2016.7954318.

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Hu, Sigui. "Optimum truncated sequential test of binomial distribution." In 2011 9th International Conference on Reliability, Maintainability and Safety (ICRMS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icrms.2011.5979278.

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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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Li, Bing, Xianneng Li, Shingo Mabu, and Kotaro Hirasawa. "Variable Size Genetic Network Programming with Binomial Distribution." In 2011 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2011. http://dx.doi.org/10.1109/cec.2011.5949723.

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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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Ji, Jia-Wei, Qiang Yang, Xu-Dong Gao, Peilan Xu, and Zhen-Yu Lu. "Binomial Distribution Assisted Individual Selection for Differential Evolution." In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023. http://dx.doi.org/10.1109/smc53992.2023.10394491.

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Prasongporn, Pralongpol, and Winai Bodhisuwan. "Negative Binomial - Two Parameter Weighted Exponential (NB-TWE) Distribution." In 5th Annual International Conference on Operations Research and Statistics (ORS 2017). Global Science & Technology Forum (GSTF), 2017. http://dx.doi.org/10.5176/2251-1938_ors17.18.

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Reports on the topic "Binomial distribution"

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Tang, Victor K., Ronald B. Sindler, and Raymond M. Shirven. Bayesian Estimation of n in a Binomial Distribution. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada196623.

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Butler, Ken, and Michael Stephens. The Distribution of a Sum of Binomial Random Variables. Fort Belvoir, VA: Defense Technical Information Center, April 1993. http://dx.doi.org/10.21236/ada266969.

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Chernoff, Herman, and Eric Lander. Asymptotic Distribution of the Likelihood Ratio Test That a Mixture of Two Binomials is a Single Binomial. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada236714.

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Collins, Joseph C. Binomial Distribution: Hypothesis Testing, Confidence Intervals (CI), and Reliability with Implementation in S-PLUS. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada523927.

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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25 miles and 0.5 miles) at each selected road segment, with the number of crashes on each road segment used as the dependent variable. The generalized linear models with negative binomial distribution (GLM-based NB model) as well as the geographically weighted negative binomial regression (GWNBR) and geographically weighted negative binomial regression model with global dispersion (GWNBRg) were developed and compared. This research relied on data for 147 geographically distributed road segments for modeling and data for 49 segments for validation. The annual average daily traffic (AADT), light commercial land use, light industrial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing the teen crash frequency. Both methods have good predictive capabilities and can be used to estimate the teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity among road teen crashes and the associated risk factors.
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Guilfoyle, Michael, Ruth Beck, Bill Williams, Shannon Reinheimer, Lyle Burgoon, Samuel Jackson, Sherwin Beck, Burton Suedel, and Richard Fischer. Birds of the Craney Island Dredged Material Management Area, Portsmouth, Virginia, 2008-2020. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45604.

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This report presents the results of a long-term trend analyses of seasonal bird community data from a monitoring effort conducted on the Craney Island Dredged Material Management Area (CIDMMA) from 2008 to 2020, Portsmouth, VA. The USACE Richmond District collaborated with the College of William and Mary and the Coastal Virginia Wildlife Observatory, Waterbird Team, to conduct year-round semimonthly area counts of the CIDMMA to examine species presence and population changes overtime. This effort provides information on the importance of the area to numerous bird species and bird species’ groups and provides an index to those species and group showing significant changes in populations during the monitoring period. We identified those species regionally identified as Highest, High, and Moderate Priority Species based on their status as rare, sensitive, or in need of conservation attention as identified by the Atlantic Coast Joint Venture (ACJV), Bird Conservation Region (BCR), New England/Mid-Atlantic Bird Conservation Area (BCR 30). Of 134 ranked priority species in the region, the CIDMMA supported 102 of 134 (76%) recognized in the BCR, including 16 of 19 (84%) of Highest priority ranked species, 47 of 60 (78.3%) of High priority species, and 39 of 55 (71%) of Moderate priority species for BCR 30. All bird count and species richness data collected were fitted to a negative binomial (mean abundance) or Poisson distribution (mean species richness) and a total of 271 species and over 1.5 million birds were detected during the monitoring period. Most all bird species and species groups showed stable or increasing trends during the monitoring period. These results indicate that the CIDMMA is an important site that supports numerous avian species of local and regional conservation concern throughout the year.
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