The Experts below are selected from a list of 46782 Experts worldwide ranked by ideXlab platform
Gareth Hughes - One of the best experts on this subject based on the ideXlab platform.
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bbd computer software for fitting the beta Binomial Distribution to disease incidence data
Plant Disease, 1994Co-Authors: L V Madden, Gareth HughesAbstract:A software program for DOS-based personal computers was developed to fit the beta-Binomial Distribution to the frequency of incidence of disease. The beta-Binomial is a discrete Distribution, which is appropriate for describing aggregated or clustered binary data such as incidence. Variance-ratio 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 chi-square goodness-of-fit 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
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using the beta Binomial Distribution to describe aggregated patterns of disease incidence
Phytopathology, 1993Co-Authors: Gareth Hughes, L V MaddenAbstract:We discuss the use of the beta-Binomial Distribution for the description of plant disease incidence data, collected on the basis of scoring plants as either «diseased» or «healthy». The beta-Binomial is a discrete probability Distribution derived by regarding the probability of a plant being diseased (a constant in the Binomial Distribution) as a beta-distributed variable. An important characteristic of the beta-Binomial is that its variance is larger than that of the Binomial Distribution with the same mean. The beta-Binomial Distribution, therefore, may serve to describe aggregated disease incidence data. Using maximum likelihood, we estimated beta-Binomial 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.
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bbd computer software for fitting the beta Binomial Distribution to disease incidence data
Plant Disease, 1994Co-Authors: L V Madden, Gareth HughesAbstract:A software program for DOS-based personal computers was developed to fit the beta-Binomial Distribution to the frequency of incidence of disease. The beta-Binomial is a discrete Distribution, which is appropriate for describing aggregated or clustered binary data such as incidence. Variance-ratio 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 chi-square goodness-of-fit 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
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using the beta Binomial Distribution to describe aggregated patterns of disease incidence
Phytopathology, 1993Co-Authors: Gareth Hughes, L V MaddenAbstract:We discuss the use of the beta-Binomial Distribution for the description of plant disease incidence data, collected on the basis of scoring plants as either «diseased» or «healthy». The beta-Binomial is a discrete probability Distribution derived by regarding the probability of a plant being diseased (a constant in the Binomial Distribution) as a beta-distributed variable. An important characteristic of the beta-Binomial is that its variance is larger than that of the Binomial Distribution with the same mean. The beta-Binomial Distribution, therefore, may serve to describe aggregated disease incidence data. Using maximum likelihood, we estimated beta-Binomial 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.
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the m bayesian credible limits of the reliability derived from Binomial Distribution
Communications in Statistics-theory and Methods, 2012Co-Authors: Ming HanAbstract:This article introduces a new method, M-Bayesian credible limit method, to estimate reliability derived from Binomial Distribution, in the case of zero-failure data. Firstly, the definition of one-sided and two-sided M-Bayesian credible limits are provided, and moreover, formulas of one-sided and two-sided M-Bayesian credible limits are also provided. secondly, properties of one-sided and two-sided M-Bayesian credible limits are discussed, and we will see that the M-Bayesian 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.
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e bayesian estimation of the reliability derived from Binomial Distribution
Applied Mathematical Modelling, 2011Co-Authors: Ming HanAbstract:This paper introduces a new parameter estimation method, named E-Bayesian estimation method, to estimate reliability derived from Binomial Distribution. The definition of E-Bayesian estimation of the reliability is proposed, the formulas of E-Bayesian 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.
K. C. Chua - One of the best experts on this subject based on the ideXlab platform.
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Test of misspecification with application to negative Binomial Distribution
Computational Statistics, 2013Co-Authors: K. C. ChuaAbstract:A misspecification test based directly on Bartlett’s First Identity is examined. This test is exemplified by the negative Binomial Distribution. A Monte Carlo simulation study has been conducted, in the context of testing Distributional misspecification, and the performance of the proposed test has been benchmarked with some goodness-of-fit tests based on the empirical Distribution function. The results suggest that the proposed test is viable in terms of computational speed and statistical power, and has the advantage that complications arising from the use of the covariance matrix in White’s information matrix test are avoided.
Markos A Katsoulakis - One of the best experts on this subject based on the ideXlab platform.
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Binomial Distribution based τ leap accelerated stochastic simulation
Journal of Chemical Physics, 2005Co-Authors: Abhijit Chatterjee, Dionisios G Vlachos, Markos A KatsoulakisAbstract:Recently, Gillespie introduced the τ-leap approximate, accelerated stochastic Monte Carlo method for well-mixed reacting systems [J. Chem. Phys. 115, 1716 (2001)]. In each time increment of that method, one executes a number of reaction events, selected randomly from a Poisson Distribution, to enable simulation of long times. Here we introduce a Binomial Distribution τ-leap algorithm (abbreviated as BD-τ method). This method combines the bounded nature of the Binomial Distribution variable with the limiting reactant and constrained firing concepts to avoid negative populations encountered in the original τ-leap method of Gillespie for large time increments, and thus conserve mass. Simulations using prototype reaction networks show that the BD-τ method is more accurate than the original method for comparable coarse-graining in time.
