• 제목/요약/키워드: mathematical distribution

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UNIVALENT FUNCTIONS WITH POSITIVE COEFFICIENTS INVOLVING POISSON DISTRIBUTION SERIES

  • Murugusundaramoorthy, Gangadharan
    • 호남수학학술지
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    • 제40권3호
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    • pp.529-538
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    • 2018
  • The purpose of the present paper is to establish connections between various subclasses of analytic univalent functions by applying certain convolution operator involving Poisson distribution series. To be more precise,we investigate such connections with the classes of analytic univalent functions with positive coefficients in the open unit disk.

GEOMETRY OF HALF LIGHTLIKE SUBMANIFOLDS OF A SEMI-RIEMANNIAN SPACE FORM WITH A SEMI-SYMMETRIC METRIC CONNECTION

  • Jin, Dae Ho
    • 충청수학회지
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    • 제24권4호
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    • pp.769-781
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    • 2011
  • We study the geometry of half lightlike sbmanifolds M of a semi-Riemannian space form $\tilde{M}(c)$ admitting a semi-symmetric metric connection subject to the conditions: (1) The screen distribution S(TM) is totally umbilical (geodesic) and (2) the co-screen distribution $S(TM^{\bot})$ of M is a conformal Killing one.

CERTAIN SUBCLASSES OF ANALYTIC FUNCTIONS ASSOCIATED WITH MILLER-ROSS-TYPE POISSON DISTRIBUTION SERIES

  • Bilal, SEKER;Sevtap, SUMER EKER;Bilal, CEKIC
    • 호남수학학술지
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    • 제44권4호
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    • pp.504-512
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    • 2022
  • The purpose of the present paper is to obtain some sufficient conditions for analytic functions, whose coefficients are probabilities of the Miller-Ross type-Poisson distribution series, to belong to classes 𝓖(λ, 𝛿) and 𝓚(λ, 𝛿).

Mathematical modeling of wind power estimation using multiple parameter Weibull distribution

  • Chalamcharla, Seshaiah C.V.;Doraiswamy, Indhumathy D.
    • Wind and Structures
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    • 제23권4호
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    • pp.351-366
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    • 2016
  • Nowadays, wind energy is the most rapidly developing technology and energy source and it is reusable. Due to its cleanliness and reusability, there have been rapid developments made on transferring the wind energy systems to electric energy systems. Converting the wind energy to electrical energy can be done only with the wind turbines. So installing a wind turbine depends on the wind speed at that location. The expected wind power can be estimated using a perfect probability distribution. In this paper Weibull and Weibull distribution with multiple parameters has been used in deriving the mathematical expression for estimating the wind power. Statistically the parameters of Weibull and Weibull distribution are estimated using the maximum likelihood techniques. We derive a probability distribution for the power output of a wind turbine with given rated wind speeds for the regions where the wind speed histograms present a bimodal pdf and compute the first order moment of this distribution.

Generalized half-logistic Poisson distributions

  • Muhammad, Mustapha
    • Communications for Statistical Applications and Methods
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    • 제24권4호
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    • pp.353-365
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    • 2017
  • In this article, we proposed a new three-parameter distribution called generalized half-logistic Poisson distribution with a failure rate function that can be increasing, decreasing or upside-down bathtub-shaped depending on its parameters. The new model extends the half-logistic Poisson distribution and has exponentiated half-logistic as its limiting distribution. A comprehensive mathematical and statistical treatment of the new distribution is provided. We provide an explicit expression for the $r^{th}$ moment, moment generating function, Shannon entropy and $R{\acute{e}}nyi$ entropy. The model parameter estimation was conducted via a maximum likelihood method; in addition, the existence and uniqueness of maximum likelihood estimations are analyzed under potential conditions. Finally, an application of the new distribution to a real dataset shows the flexibility and potentiality of the proposed distribution.

THE MOMENTS OF THE RIESZ-NǺGY-TAKǺCS DISTRIBUTION OVER A GENERAL INTERVAL

  • Baek, In-Soo
    • 대한수학회보
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    • 제47권1호
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    • pp.187-193
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    • 2010
  • In this paper, the moments of the Riesz-N$\acute{a}$gy-Tak$\acute{a}$cs(RNT) distribution over a general interval [a, b] $\subset$ [0, 1], are found through the moments of the RNT distribution over the unit interval, [0, 1]. This is done using some special features of the distribution and the fact that [0, 1] is a self-similar set in a dynamical system generated by the RNT distribution. The results are important for the study of the orthogonal polynomials with respect to the RNT distribution over a general interval.