• Title/Summary/Keyword: distribution function

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Studies on the Application of Weibull Distribution to Forestry (II) - Estimation of Parameter by Gamma Function - (Weibull 분포(分布)를 응용(應用)한 임학연구(林學硏究)(II) - Gamma함수(函數)에 의한 parameter의 추정(推定) -)

  • Yun, Jong Wha
    • Journal of Korean Society of Forest Science
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    • v.61 no.1
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    • pp.1-7
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    • 1983
  • In the estimation of diameter distribution in a stand using Weibull distribution function, the calculation method of experimental distribution was presented in previous paper. This study was to estimate the diameter distribution of Korean pine stands by Weibull distribution which represents Gamma function, with mean diameter and mean basal-area diameter of the random sample trees. The results obtained fitted the diameter distribution in experimental stands. Thus, this method appears to be used for the estimation of diameter distribution in a stand as well as for the analysis and prediction of stand construction for the future.

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On Asymptotic Properties of a Maximum Likelihood Estimator of Stochastically Ordered Distribution Function

  • Oh, Myongsik
    • Communications for Statistical Applications and Methods
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    • v.20 no.3
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    • pp.185-191
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    • 2013
  • Kiefer (1961) studied asymptotic behavior of empirical distribution using the law of the iterated logarithm. Robertson and Wright (1974a) discussed whether this type of result would hold for a maximum likelihood estimator of a stochastically ordered distribution function; however, we show that this cannot be achieved. We provide only a partial answer to this problem. The result is applicable to both estimation and testing problems under the restriction of stochastic ordering.

OPTIMAL APPROXIMATION BY ONE GAUSSIAN FUNCTION TO PROBABILITY DENSITY FUNCTIONS

  • Gwang Il Kim;Seung Yeon Cho;Doobae Jun
    • East Asian mathematical journal
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    • v.39 no.5
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    • pp.537-547
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    • 2023
  • In this paper, we introduce the optimal approximation by a Gaussian function for a probability density function. We show that the approximation can be obtained by solving a non-linear system of parameters of Gaussian function. Then, to understand the non-normality of the empirical distributions observed in financial markets, we consider the nearly Gaussian function that consists of an optimally approximated Gaussian function and a small periodically oscillating density function. We show that, depending on the parameters of the oscillation, the nearly Gaussian functions can have fairly thick heavy tails.

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.

Measurement of Spatial coherence function and Directional coherence function of Propagating Laser Beam by using Wigner Distribution Function

  • Lee, Chang-Hyuck;Kang, Yoon-Shik;Noh, Jae-Woo
    • Proceedings of the Optical Society of Korea Conference
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    • 2009.02a
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    • pp.449-450
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    • 2009
  • The spatial coherence and propagation property of laser beam propagating through several optical components were studied experimentally by using the measurement of Wigner distribution function. It is shown experimentally that the Wigner function measurement yields total degree of coherence, beam quality parameter, and the near and the far field information of the propagating beam. More complete characterization of the laser beam was achieved by applying the Schmidt mode decomposition to the Wigner distribution function, spatial coherence function and directional coherence function. Fine details of coherence property are understood by the characteristics of the contributing eigenmodes.

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Some Exponentiated Distributions

  • Ali, M. Masoom;Pal, Manisha;Woo, Jung-Soo
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.93-109
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    • 2007
  • In this paper we study a number of new exponentiated distributions. The survival function, failure rate and moments of the distributions have been derived using certain special functions. The behavior of the failure rate has also been studied.

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.

A GENERALIZED SINGULAR FUNCTION

  • Baek, In-Soo
    • Journal of the Chungcheong Mathematical Society
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    • v.23 no.4
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    • pp.657-661
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    • 2010
  • We study a singular function which we call a generalized cylinder convex(concave) function induced from different generalized dyadic expansion systems on the unit interval. We show that the generalized cylinder convex(concave)function is a singular function and the length of its graph is 2. Using a local dimension set in the unit interval, we give some characterization of the distribution set using its derivative, which leads to that this singular function is nowhere differentiable in the sense of topological magnitude.

Posterior Inference in Single-Index Models

  • Park, Chun-Gun;Yang, Wan-Yeon;Kim, Yeong-Hwa
    • Communications for Statistical Applications and Methods
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    • v.11 no.1
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    • pp.161-168
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    • 2004
  • A single-index model is useful in fields which employ multidimensional regression models. Many methods have been developed in parametric and nonparametric approaches. In this paper, posterior inference is considered and a wavelet series is thought of as a function approximated to a true function in the single-index model. The posterior inference needs a prior distribution for each parameter estimated. A prior distribution of each coefficient of the wavelet series is proposed as a hierarchical distribution. A direction $\beta$ is assumed with a unit vector and affects estimate of the true function. Because of the constraint of the direction, a transformation, a spherical polar coordinate $\theta$, of the direction is required. Since the posterior distribution of the direction is unknown, we apply a Metropolis-Hastings algorithm to generate random samples of the direction. Through a Monte Carlo simulation we investigate estimates of the true function and the direction.