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Binomial Distribution
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Gareth Hughes – One of the best experts on this subject based on the ideXlab platform.

bbd computer software for fitting the beta Binomial Distribution to disease incidence data
Plant Disease, 1994CoAuthors: L V Madden, Gareth HughesAbstract:A software program for DOSbased personal computers was developed to fit the betaBinomial Distribution to the frequency of incidence of disease. The betaBinomial is a discrete Distribution, which is appropriate for describing aggregated or clustered binary data such as incidence. Varianceratio and C(α) tests are performed to determine if there is evidence that incidence is aggregated. The program then calculates Distribution parameters and their standard errors using a maximum likelihood procedure, determines the expected values of the Distribution, and calculates a chisquare goodnessoffit test. For comparison purposes, the program fits the Binomial Distribution to the same data. The software and a detailed user’s manual are available free from either author

using the beta Binomial Distribution to describe aggregated patterns of disease incidence
Phytopathology, 1993CoAuthors: Gareth Hughes, L V MaddenAbstract:We discuss the use of the betaBinomial Distribution for the description of plant disease incidence data, collected on the basis of scoring plants as either «diseased» or «healthy». The betaBinomial is a discrete probability Distribution derived by regarding the probability of a plant being diseased (a constant in the Binomial Distribution) as a betadistributed variable. An important characteristic of the betaBinomial is that its variance is larger than that of the Binomial Distribution with the same mean. The betaBinomial Distribution, therefore, may serve to describe aggregated disease incidence data. Using maximum likelihood, we estimated betaBinomial parameters p (mean disease incidence) and θ (an index of aggregation) for four previously published sets of disease incidence data in which there were some indications of aggregation […]
L V Madden – One of the best experts on this subject based on the ideXlab platform.

bbd computer software for fitting the beta Binomial Distribution to disease incidence data
Plant Disease, 1994CoAuthors: L V Madden, Gareth HughesAbstract:A software program for DOSbased personal computers was developed to fit the betaBinomial Distribution to the frequency of incidence of disease. The betaBinomial is a discrete Distribution, which is appropriate for describing aggregated or clustered binary data such as incidence. Varianceratio and C(α) tests are performed to determine if there is evidence that incidence is aggregated. The program then calculates Distribution parameters and their standard errors using a maximum likelihood procedure, determines the expected values of the Distribution, and calculates a chisquare goodnessoffit test. For comparison purposes, the program fits the Binomial Distribution to the same data. The software and a detailed user’s manual are available free from either author

using the beta Binomial Distribution to describe aggregated patterns of disease incidence
Phytopathology, 1993CoAuthors: Gareth Hughes, L V MaddenAbstract:We discuss the use of the betaBinomial Distribution for the description of plant disease incidence data, collected on the basis of scoring plants as either «diseased» or «healthy». The betaBinomial is a discrete probability Distribution derived by regarding the probability of a plant being diseased (a constant in the Binomial Distribution) as a betadistributed variable. An important characteristic of the betaBinomial is that its variance is larger than that of the Binomial Distribution with the same mean. The betaBinomial Distribution, therefore, may serve to describe aggregated disease incidence data. Using maximum likelihood, we estimated betaBinomial parameters p (mean disease incidence) and θ (an index of aggregation) for four previously published sets of disease incidence data in which there were some indications of aggregation […]
Ming Han – One of the best experts on this subject based on the ideXlab platform.

the m bayesian credible limits of the reliability derived from Binomial Distribution
Communications in Statisticstheory and Methods, 2012CoAuthors: Ming HanAbstract:This article introduces a new method, MBayesian credible limit method, to estimate reliability derived from Binomial Distribution, in the case of zerofailure data. Firstly, the definition of onesided and twosided MBayesian credible limits are provided, and moreover, formulas of onesided and twosided MBayesian credible limits are also provided. secondly, properties of onesided and twosided MBayesian credible limits are discussed, and we will see that the MBayesian credible limit method is superior to the corresponding classical confidence limit method. Finally, the new estimation method is applied to a numerical example. Through the example the efficiency and easiness of operation of this method are commended.

e bayesian estimation of the reliability derived from Binomial Distribution
Applied Mathematical Modelling, 2011CoAuthors: Ming HanAbstract:This paper introduces a new parameter estimation method, named EBayesian estimation method, to estimate reliability derived from Binomial Distribution. The definition of EBayesian estimation of the reliability is proposed, the formulas of EBayesian estimation and hierarchical Bayesian estimation of the reliability are also provided. Finally, it is shown, through a numerical example, that the new method is much simpler than hierarchical Bayesian estimation in practice.