• Title/Summary/Keyword: hypergeometric distribution

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SOME PROPERTIES OF BIVARIATE GENERALIZED HYPERGEOMETRIC PROBABILITY DISTRIBUTIONS

  • Kumar, C. Satheesh
    • Journal of the Korean Statistical Society
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    • v.36 no.3
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    • pp.349-355
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    • 2007
  • In this paper we study some important properties of the bivariate generalized hypergeometric probability (BGHP) distribution by establishing the existence of all the moments of the distribution and by deriving recurrence relations for raw moments. It is shown that certain mixtures of BGHP distributions are again BGHP distributions and a limiting case of the distribution is considered.

GENERALIZATION OF EXTENDED BETA FUNCTION, HYPERGEOMETRIC AND CONFLUENT HYPERGEOMETRIC FUNCTIONS

  • Lee, Dong-Myung;Rathie, Arjun K.;Parmar, Rakesh K.;Kim, Yong-Sup
    • Honam Mathematical Journal
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    • v.33 no.2
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    • pp.187-206
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    • 2011
  • The main object of this paper is to present generalization of extended beta function, extended hypergeometric and confluent hypergeometric function introduced by Chaudhry et al. and obtained various integral representations, properties of beta function, Mellin transform, beta distribution, differentiation formulas transform formulas, recurrence relations, summation formula for these new generalization.

Confluent Hypergeometric Distribution and Its Applications on Certain Classes of Univalent Functions of Conic Regions

  • Porwal, Saurabh
    • Kyungpook Mathematical Journal
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    • v.58 no.3
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    • pp.495-505
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    • 2018
  • The purpose of the present paper is to investigate Confluent hypergeometric distribution. We obtain some basic properties of this distribution. It is worthy to note that the Poisson distribution is a particular case of this distribution. Finally, we give a nice application of this distribution on certain classes of univalent functions of the conic regions.

On the actual coverage probability of hypergeometric parameter (초기하분포의 모수에 대한 신뢰구간추정)

  • Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.6
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    • pp.1109-1115
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    • 2010
  • In this paper, exact confidence interval of hyper-geometric parameter, that is the probability of success p in the population is discussed. Usually, binomial distribution is a well known discrete distribution with abundant usage. Hypergeometric distribution frequently replaces a binomial distribution when it is desirable to make allowance for the finiteness of the population size. For example, an application of the hypergeometric distribution arises in describing a probability model for the number of children attacked by an infectious disease, when a fixed number of them are exposed to it. Exact confidence interval estimation of hypergeometric parameter is reviewed. We consider the performance of exact confidence interval estimates of hypergeometric parameter in terms of actual coverage probability by small sample Monte Carlo simulation.

Customized Model of Cold Chain Logistics Considering Hypergeometric Distribution

  • Chen, Xing;Chuluunsukh, Anudari;Jang, Jun-Ho
    • Journal of Korea Society of Industrial Information Systems
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    • v.26 no.5
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    • pp.37-54
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    • 2021
  • In this study, a customized model (CM) for the efficient operation of cold chain logistics considering the hypergeometric distribution is proposed. The CM focuses on the segmentation market of ready-to-eat foods and juices made from fresh materials. Companies should determine the amount of production by predicting consumer preferences and quantity to ensure high-efficiency production. The CM is represented as a mathematical formulation and implemented using the genetic algorithm (GA). Addition, the relative weights of CM are calculated. Further, the calculated weights are applied to the GA. In the numerical experiment, hypergeometric distribution is used to calculate the relative weights between the range of production quantities and the customized amount. Experiment results are the values of relative weights and the comparison results by average values of handling cost, total cost and CPU time. Finally, the significance of this study is summarized and a future research direction is remarked in conclusion.

Reliability Demonstration Test for a Finite Population Based on the Conjugacy of the Beta-Binomial Distribution (베타-이항분포의 공액성을 근거로 한 유한 모집단의 신뢰성 입증 시험)

  • Jeon, Jong-Seon;Ahn, Sun-Eung
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.35 no.2
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    • pp.98-105
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    • 2012
  • This paper describes the Bayesian approach for reliability demonstration test based on the samples from a finite population. The Bayesian approach involves the technical method about how to combine the prior distribution and the likelihood function to produce the posterior distribution. In this paper, the hypergeometric distribution is adopted as a likelihood function for a finite population. The conjugacy of the beta-binomial distribution and the hypergeometric distribution is shown and is used to make a decision about whether to accept or reject the finite population judging from a viewpoint of faulty goods. A numerical example is also given.

A simulation study for the approximate confidence intervals of hypergeometric parameter by using actual coverage probability (실제포함확률을 이용한 초기하분포 모수의 근사신뢰구간 추정에 관한 모의실험 연구)

  • Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.6
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    • pp.1175-1182
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    • 2011
  • In this paper, properties of exact confidence interval and some approximate confidence intervals of hyper-geometric parameter, that is the probability of success p in the population is discussed. Usually, binomial distribution is a well known discrete distribution with abundant usage. Hypergeometric distribution frequently replaces a binomial distribution when it is desirable to make allowance for the finiteness of the population size. For example, an application of the hypergeometric distribution arises in describing a probability model for the number of children attacked by an infectious disease, when a fixed number of them are exposed to it. Exact confidence interval estimation of hypergeometric parameter is reviewed. We consider the approximation of hypergeometirc distribution to the binomial and normal distribution respectively. Approximate confidence intervals based on these approximation are also adequately discussed. The performance of exact confidence interval estimates and approximate confidence intervals of hypergeometric parameter is compared in terms of actual coverage probability by small sample Monte Carlo simulation.

On Reliability and Ratio in the Beta Case

  • Woo, Jung-Soo
    • Communications for Statistical Applications and Methods
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    • v.16 no.3
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    • pp.541-547
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    • 2009
  • We consider distribution, reliability and moment of ratio in two independent beta random variables X and Y, and reliability and $K^{th}$ moment of ratio are represented by a mathematical generalized hypergeometric function. We introduce an approximate maximum likelihood estimate(AML) of reliability and right-tail probability in the beta distribution.

HYPERGEOMETRIC DISTRIBUTION SERIES AND ITS APPLICATION OF CERTAIN CLASS OF ANALYTIC FUNCTIONS BASED ON SPECIAL FUNCTIONS

  • Murugusundaramoorthy, Gangadharan;Porwal, Saurabh
    • Communications of the Korean Mathematical Society
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    • v.36 no.4
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    • pp.671-684
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    • 2021
  • The tenacity of the current paper is to find connections between various subclasses of analytic univalent functions by applying certain convolution operator involving generalized hypergeometric distribution series. To be more specific, we examine such connections with the classes of analytic univalent functions k - 𝓤𝓒𝓥* (𝛽), k - 𝓢*p (𝛽), 𝓡 (𝛽), 𝓡𝜏 (A, B), k - 𝓟𝓤𝓒𝓥* (𝛽) and k - 𝓟𝓢*p (𝛽) in the open unit disc 𝕌.

Inference on the reliability P(Y < X) in the gamma case

  • Moon, Yeung-Gil;Lee, Chang-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.1
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    • pp.219-223
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    • 2009
  • We shall derive a quotient distribution of two independent gamma variables and its moment and reliability are represented by hypergeometric function and Wittaker's function. And we shall consider an inference on the reliability in two independent gamma random variables.

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