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Relevant bibliographies by topics / Beta-Binomial Model

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Academic literature on the topic 'Beta-Binomial Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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Journal articles on the topic "Beta-Binomial 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 (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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Kim, Jongphil, and Ji-Hyun Lee. "The validation of a beta-binomial model for overdispersed binomial data." Communications in Statistics - Simulation and Computation 46, no. 2 (2016): 807–14. http://dx.doi.org/10.1080/03610918.2014.960091.

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Hilton, Joe, and Ian Hall. "A beta-Poisson model for infectious disease transmission." PLOS Computational Biology 20, no. 2 (2024): e1011856. http://dx.doi.org/10.1371/journal.pcbi.1011856.

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Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in whic

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SILVERMAN, B. W., and J. D. WILSON. "A BETA‐BINOMIAL MODEL FOR LIBRARY SURVEY DATA." Journal of Documentation 43, no. 2 (1987): 112–24. http://dx.doi.org/10.1108/eb026804.

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Tripathi, Ram C., Ramesh C. Gupta, and John Gurland. "Estimation of parameters in the beta binomial model." Annals of the Institute of Statistical Mathematics 46, no. 2 (1994): 317–31. http://dx.doi.org/10.1007/bf01720588.

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SCHUCKERS, MICHAEL E. "USING THE BETA-BINOMIAL DISTRIBUTION TO ASSESS PERFORMANCE OF A BIOMETRIC IDENTIFICATION DEVICE." International Journal of Image and Graphics 03, no. 03 (2003): 523–29. http://dx.doi.org/10.1142/s0219467803001147.

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This paper discusses the use of the Beta-binomial distribution to estimate the matching performance of a biometric identification device. Specifically, the Beta-binomial distribution can be used to assess the variability in estimates of the false match and the false non-match rates when multiple users are tested more than once. This method accounts for the extraneous variability in this scenario and allows for the creation of confidence intervals under certain regularity conditions. The Beta-binomial differs from the binomial in that it models the extra-variation that is due to a lack of margi

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Mamun, Abdulla, and Sudhir Paul. "Model Selection in Generalized Linear Models." Symmetry 15, no. 10 (2023): 1905. http://dx.doi.org/10.3390/sym15101905.

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The problem of model selection in regression analysis through the use of forward selection, backward elimination, and stepwise selection has been well explored in the literature. The main assumption in this, of course, is that the data are normally distributed and the main tool used here is either a t test or an F test. However, the properties of these model selection procedures are not well-known. The purpose of this paper is to study the properties of these procedures within generalized linear regression models, considering the normal linear regression model as a special case. The main tool

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Im, Seongah. "Performance of the Beta-Binomial Model for Clustered Binary Responses: Comparison with Generalized Estimating Equations." Journal of Modern Applied Statistical Methods 19, no. 1 (2021): 2–25. http://dx.doi.org/10.22237/jmasm/1619482380.

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This study examined performance of the beta-binomial model in comparison with GEE using clustered binary responses resulting in non-normal outcomes. Monte Carlo simulations were performed under varying intracluster correlations and sample sizes. The results showed that the beta-binomial model performed better for small sample, while GEE performed well under large sample.

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Lee, Jack C., and Darius J. Sabavala. "Bayesian Estimation and Prediction for the Beta-Binomial Model." Journal of Business & Economic Statistics 5, no. 3 (1987): 357. http://dx.doi.org/10.2307/1391611.

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Cepeda-Cuervo, Edilberto, and Maria Victoria Cifuentes-Amado. "Tilted Beta Binomial Linear Regression Model: A Bayesian Approach." Journal of Mathematics and Statistics 16, no. 1 (2020): 1–8. http://dx.doi.org/10.3844/jmssp.2020.1.8.

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Dissertations / Theses on the topic "Beta-Binomial Model"

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Um, Jong Seok. "Conditional inference about kappa in the beta-binomial model /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487757723997994.

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Niangoran, Bessekon. "Apport du monitorage statistique des données dans la gestion des essais cliniques multicentriques en Afrique." Electronic Thesis or Diss., Bordeaux, 2023. http://www.theses.fr/2023BORD0436.

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La qualité des données est une préoccupation fondamentale de la recherche clinique. Pour garantir cette qualité, il faut pratiquer un monitorage continu des données. Les organismes internationaux de régulation des médicaments recommandent que ce monitorage soit ciblé, basé sur une analyse des risques. De cette recommandation a germé le concept de « monitoring statistique centralisé » (MSC) qui consiste à détecter des distributions de variables atypiques dans un centre par rapport aux autres centres. Cette thèse recense les méthodes de MSC existants, en propose de nouvelles, et compare les perf

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Lora, Mayra Ivanoff. "Modelos de regressão beta-binomial/poisson para contagens bivariadas." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-09062011-095707/.

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Propomos um modelo Beta-Binomial/Poisson para dados provenientes de um estudo com doentes de Parkinson, que consistiu em contar durante um minuto quantas tarefas foram realizadas e destas, quantas de maneira correta, antes e depois de um treinamento. O objetivo era verificar se o treinamento aumentava o número de tentativas e a porcentagem de acerto, o que destaca o aspecto bivariado do problema. Esse modelo considera tal aspecto, usa uma distribuição mais adequada a dados de contagem e ainda suporta a sobredispersão presente nos dados. Obtemos estimadores de máxima verossimilhança dos parâmet

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Lora, Mayra Ivanoff. "Modelos Beta-Binomial/Poisson-Gama para contagens bivariadas repetidas." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-27082009-120419/.

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Em Lora e Singer (Statistics in Medicine, 2008), propusemos um modelo Beta- Binomial/Poisson p-variado para análise dos dados provenientes de um estudo que consistiu em contar o número de tentativas e acertos de um exercício manual com duração de um minuto realizado por doentes de Parkinson, antes e depois de um treinamento. O objetivo era verificar se o treinamento aumentava o número de tentativas e a porcentagem de acerto, o que destaca o aspecto bivariado do problema. Esse modelo leva tais características em consideração, usa uma distribuição adequada para dados de contagem e ainda acomoda

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SAJJAD, MALIK INTISAR ALI. "Characterisation and Flexibility Assessment of Aggregate Electrical Demand." Doctoral thesis, Politecnico di Torino, 2015. http://hdl.handle.net/11583/2594365.

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The renewable energy sources (RES) are intermittent in their nature and their integration in electric power grid has introduced the mismatch between supply and demand. This mismatch can be leveled by using the flexibilities from the supply and the demand side. The demand side in a power system has key importance in the evolving context of the energy systems. Electrical load patterns that represent the consumption level are affected by different types of uncertainties associated with customer’s behavior and with keeping acceptable comfort level. The resulting aggregated load pattern indicates t

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Shannon, Carlie. "A case study in applying generalized linear mixed models to proportion data from poultry feeding experiments." Kansas State University, 2013. http://hdl.handle.net/2097/15519.

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Master of Science<br>Department of Statistics<br>Leigh Murray<br>This case study was motivated by the need for effective statistical analysis for a series of poultry feeding experiments conducted in 2006 by Kansas State University researchers in the department of Animal Science. Some of these experiments involved an automated auger feed line system commonly used in commercial broiler houses and continuous, proportion response data. Two of the feed line experiments are considered in this case study to determine if a statistical model using a non-normal response offers a better fit for this data

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MATTOS, ROGERIO SILVA DE. "DATA DISAGGREGATION WITH ECOLOGICAL INFERENCE: IMPLEMENTATION OF MODELS BASED IN THE TRUNCATED NORMAL AND ON THE BINOMIAL-BETA VIA EM ALGORITHM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2000. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=1347@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Inferência ecológica reúne o conjunto de procedimentos estatísticos para se prever dados desagregados quando só estão disponíveis dados agregados. Duas novas metodologias propostas recentemente vêm motivando novos desenvolvimentos na área: o modelo baseado na normal bivariada truncada (MNBT) e o modelo hierárquico binomial-beta (MHBB). A tese reavalia estas metodologias e explora implementações computacionais mais eficientes através do Algoritmo EM e uma de suas extensões, o Algoritmo ECM. Comparando-se com métodos de qua

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Cheong, Yunjae 1976. "Multivariate beta binomial distribution model as a web media exposure model." Thesis, 2007. http://hdl.handle.net/2152/3215.

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This study develops and tests a new multivariate distribution model for the estimation of advertising vehicle exposure. The new multivariate distribution model is developed as three versions (i.e., one which doesn't adjust negative probabilities, and the others which adjust negative probabilities in unvariate distributions). In addition, eight other media exposure models are evaluated against a database of 440 tabulated schedules constructed from 2003 comScore network data. The types of models tested include: three univariate models -- the Binomial Distribution Model (BIN), the Beta Binomial D

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"Bayesian Updating and Statistical Inference for Beta-binomial Models." Tulane University, 2018.

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acase@tulane.edu<br>The Beta-binomial distribution is often employed as a model for count data in cases where the observed dispersion is greater than would be expected for the standard binomial distribution. Parameter estimation in this setting is typically performed using a Bayesian approach, which requires specifying appropriate prior distributions for parameters. In the context of many applications, incorporating estimates from previous analyses can offer advantages over naive or diffuse priors. An example of this is in the food security setting, where baseline consumption surveys can infor

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"Data disaggregation with ecological inference: implementation of models based in the truncated normal and on the binomial-beta via em algorithm." Tese, MAXWELL, 2000. http://www.maxwell.lambda.ele.puc-rio.br/cgi-bin/db2www/PRG_0991.D2W/SHOW?Cont=1347:pt&Mat=&Sys=&Nr=&Fun=&CdLinPrg=pt.

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Books on the topic "Beta-Binomial Model"

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Srivastava, M. S. On beta-binomial model for extrabinomial variation. Dept. of Statistics, University of Toronto, 1990.

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Hanson, Bradley A. Method of moments estimates for the four-parameter beta compound binomial model and the calculation of classification consistency indexes. American College Testing Program, 1991.

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Book chapters on the topic "Beta-Binomial Model"

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Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. "The Beta-Binomial Bayesian Model." In Bayes Rules! Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429288340-3.

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Chen, Lu, and Balgobin Nandram. "A Hierarchical Bayesian Beta-Binomial Model for Sub-areas." In Springer Proceedings in Mathematics & Statistics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7932-2_2.

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Polasek, Wolfgang. "Das Beta-Binomial-Modell." In Schließende Statistik. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59099-3_4.

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Salinas Ruíz, Josafhat, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, and Jose Crossa Hiriart. "Generalized Linear Mixed Models for Proportions and Percentages." In Generalized Linear Mixed Models with Applications in Agriculture and Biology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32800-8_6.

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AbstractIn this chapter, we will review generalized linear mixed models (GLMMs) whose response can be either a proportion or a percentage. For proportion and percentage data, we refer to data whose expected value is between 0 and 1 or between 0 and 100. For the remainder of this book, we will refer to this type of data only in terms of proportion, knowing that it is possible to change it to a percentage scale only when multiplying it by 100. Proportions can be classified into two types: discrete and continuous. Discrete proportions arise when the unit of observation consists of N distinct entities, of which individuals have the attribute of interest “y. ”N must be a nonnegative integer and “y” must be a positive integer; here, y ≤ N. Therefore, the observed proportion must be a discrete fraction, which can take values $$ \frac{0}{N},\frac{1}{N},\cdots, \frac{N}{N} $$ 0 N , 1 N , ⋯ , N N . A binomial distribution is the sum of a series of m independent binary trials (i.e., trials with only two possible outcomes: success or failure), where all trials have the same probability of success. For binary and binomial distributions, the target of inference is the value of the parameter such that $$ 0\le E\left(\frac{y}{N}\right)=\pi \le 1 $$ 0 ≤ E y N = π ≤ 1 . Continuous proportions (ratios) arise when the researcher measures responses such as the fraction of the area of a leaf infested with a fungus, the proportion of damaged cloth in a square meter, the fraction of a contaminated area, and so on. As with the binomial parameter π, the continuous rates (fractions) take values between 0 and 1, but, unlike the binomial, the continuous proportions do not result from a set of Bernoulli tests. Instead, the beta distribution is most often used when the response variable is in continuous proportions. In the following sections, we will first address issues in modeling when we have binary and binomial data. When the response variable is binomial, we have the option of using a linearization method (pseudo-likelihood (PL)) or the Laplace or quadrature integral approximation (Stroup 2012).

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Germanas, Sarunas, Audrone Jakaitiene, and Mario Guarracino. "Detection of Rare Mutations Using Beta-Binomial and Empirical Quantile Models in Next-Generation Sequencing Experiments." In Dynamics of Mathematical Models in Biology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45723-9_8.

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"The Beta-Binomial model." In Introduction to Hierarchical Bayesian Modeling for Ecological Data. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b12501-4.

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"Replicated discrimination tests: beta-binomial model." In Sensory Discrimination Tests and Measurements. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118994863.ch9.

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"Replicated discrimination tests: corrected beta-binomial model." In Sensory Discrimination Tests and Measurements. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118994863.ch10.

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Risko, Kenneth J., and Barry H. Margolin. "Some observations on detecting extra-binomial variability within the beta-binomial model." In Statistics in Toxicology. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198523291.003.0005.

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Abstract The study of methods of analysis for over-dispersed binomial data has received much attention in the past decade. A substantial portion of that effort has centred on models in which the increased variability is attributable to the heterogeneity of the binomial parameter p across observations.

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Essington, Timothy E. "Random Variables and Probability." In Introduction to Quantitative Ecology. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192843470.003.0007.

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The chapter “Random Variables and Probability” serves as both a review and a reference on probability. The random variable is the core concept in understanding probability, parameter estimation, and model selection. This chapter reviews the basic idea of a random variable and discusses the two main kinds of random variables: discrete random variables and continuous random variables. It covers the distinction between discrete and continuous random variables and outlines the most common probability mass or density functions used in ecology. Advanced sections cover distributions such as the gamma distribution, Student’s t-distribution, the beta distribution, the beta-binomial distribution, and zero-inflated models.

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Conference papers on the topic "Beta-Binomial Model"

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Rohimah, Siti Rohmah, Khairil Anwar Notodiputro, and Bagus Sartono. "Comparison between binomial generalized linear mixmodels (binomial GLMM) and Beta-Binomial hierarchical generalized linear model (Beta- BinomialHGLM) for modeling poverty data in West Java." In INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0112033.

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Lowe, Stephen A. "The beta-binomial mixture model for word frequencies in documents with applications to information retrieval." In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-537.

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"Beta negative binomial mixture model facilitates identification of allele-specific gene regulation in high-throughput sequencing data." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-664.

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Huang, Zhaofeng, and Yan Jin. "A Prior and Data Validation and Adjustment Scheme for Bayesian Reliability Analysis in Engineering Design." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28847.

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Bayesian reliability analysis (BRA) technique has been actively used in reliability assessment for engineered systems. However, there are two key controversies surrounding the BRA, that is, the reasonableness of the prior, and the consistency among all data sets. These issues have been debated in Bayesian analysis for many years, and as we observed, they have not been resolved satisfactorily. These controversies have seriously hindered the applications of BRA as a useful reliability analysis tool to support engineering design. In this paper, a Bayesian reliability analysis methodology with a p

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Dogucu, Mine, and Alicia Johnson. "Supporting Bayesian Modeling With Visualizations." 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.t6c2.

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With computational advances, Bayesian modeling is becoming more accessible. But because Bayesian thinking often differs from learners’ previous statistics training, it can be challenging for novice Bayesian learners to conceptualize and interpret the three major components of a Bayesian analysis: the prior, likelihood, and posterior. To this end, we developed an R package, bayesrules, which provides tools for exploring common introductory Bayesian models: beta-binomial, gamma-Poisson, and normal-normal. Specifically, within these model settings, the bayesrules functions provide an active learn

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Saputro, Dewi Retno Sari, Yuanita Kusuma Wardani, Nafisa Berliana Indah Pratiwi, and Purnami Widyaningsih. "Data simulation with Markov Chain Monte Carlo, Gibbs sampling, and Bayes (beta-binomial) methods as the parameter estimations of spatial bivariate probit regression model." In THE THIRD INTERNATIONAL CONFERENCE ON MATHEMATICS: Education, Theory and Application. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0040332.

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Pires, Rubiane Maria, and Carlos Alberto Ribeiro Diniz. "Bayesian residual analysis for beta-binomial regression models." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759610.

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"Performance of Beta-Binomial SGoF Multitesting Method for Dependent Gene Expression Levels - A Simulation Study." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004191100930097.

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