• Title/Summary/Keyword: distribution parameter

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A COMPARATIVE EVALUATION OF THE ESTIMATORS OF THE 2-PARAMETER GENERALIZED PARETO DISTRIBUTION

  • Singh, V.P.;Ahmad, M.;Sherif, M.M.
    • Water Engineering Research
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    • v.4 no.3
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    • pp.155-173
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    • 2003
  • Parameters and quantiles of the 2-parameter generalized Pareto distribution were estimated using the methods of regular moments, modified moments, probability weighted moments, linear moments, maximum likelihood, and entropy for Monte Carlo-generated samples. The performance of these seven estimators was statistically compared, with the objective of identifying the most robust estimator. It was found that in general the methods of probability-weighted moments and L-moments performed better than the methods of maximum likelihood estimation, moments and entropy, especially for smaller values of the coefficient of variation and probability of exceedance.

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An Anderson-Darling Goodness-of-Fit Test for the Gamma Distribution

  • Won, Hyung-Gyoo
    • Journal of Korean Society for Quality Management
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    • v.24 no.4
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    • pp.103-111
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    • 1996
  • This paper provides a test of the composite hypothesis that a random sample is (two parameter) gamma distributed when both the scale and shape parameters are estimated from the data. The test statistic is a variant of the usual Anderson-Darling statistic, the primary difference being that the statistic is based on the maximum likelihood estimator of the shape parameter of the assumed gamma distribution. The percentage points are developed via simulation and are presented graphically. Examples are provided.

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Parameter Extraction for Optimum Design of Low Noise GaAs MESFET (저잡음 GaAs MESFET의 최적화 설계를 위한 파라미터 추출)

  • 이상배
    • Journal of the Korean Institute of Navigation
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    • v.16 no.3
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    • pp.65-76
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    • 1992
  • An algorithm to determine the optimum nominal value of geometrical and material parameters in divice modelling is proposed. The algorithm uses the yield and variance prediction formula and Monte-Carlo analysis. The performance specification of the noise figure must also be satisfied. In this paper, the total number of considered devices is 1000, and each parameter of geometrical and material parameters is generated randomly within the limits of ${\pm}3%$ of nominal value, and the distribution of 1000 geometrical and material parameters is gaussing distribution.

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A Note on Parametric Bootstrap Model Selection

  • Lee, Kee-Won;Songyong Sim
    • Journal of the Korean Statistical Society
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    • v.27 no.4
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    • pp.397-405
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    • 1998
  • We develop parametric bootstrap model selection criteria in an example to fit a random sample to either a general normal distribution or a normal distribution with prespecified mean. We apply the bootstrap methods in two ways; one considers the direct substitution of estimated parameter for the unknown parameter, and the other focuses on the bias correction. These bootstrap model selection criteria are compared with AIC. We illustrate that all the selection rules reduce to the one sample t-test, where the cutoff points converge to some certain points as the sample size increases.

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A NOTE ON THE GEOMETRICAL PROPERTIES OF THE NORMAL DISTRIBUTION

  • Cho, Bong-Sik
    • Honam Mathematical Journal
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    • v.29 no.1
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    • pp.75-81
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    • 2007
  • The Fisher information matrix plays a significant role in statistical inference in connection with estimation and properties of variance of estimators. In this paper, the parameter space of the normal distribution using its Fisher's matrix is defined. The Riemannian curvature and J-divergence to parameter space are calculated.

Estimation for a bivariate survival model based on exponential distributions with a location parameter

  • Hong, Yeon Woong
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.4
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    • pp.921-929
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    • 2014
  • A bivariate exponential distribution with a location parameter is proposed as a model for a two-component shared load system with a guarantee time. Some statistical properties of the proposed model are investigated. The maximum likelihood estimators and uniformly minimum variance unbiased estimators of the parameters, mean time to failure, and the reliability function of system are obtained with unknown guarantee time. Simulation studies are given to illustrate the results.

NONINFORMATIVE PRIORS FOR PARETO DISTRIBUTION : REGULAR CASE

  • Kim, Dal-Ho;Lee, Woo-Dong;Kang, Sang-Gil
    • 한국데이터정보과학회:학술대회논문집
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    • 2003.05a
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    • pp.27-37
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    • 2003
  • In this paper, we develop noninformative priors for two parameter Pareto distribution. Specially, we derive Jeffrey's prior, probability matching prior and reference prior for the parameter of interest. In our case, the probability matching prior is only a first order and there does not exist a second order matching prior. Some simulation reveals that the matching prior performs better to achieve the coverage probability. And a real example will be given.

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A note on the geometric structure of the t-distribution

  • Cho, Bong-Sik;Jung, Sun-Young
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.3
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    • pp.575-580
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    • 2010
  • The Fisher information matrix plays a significant role in statistical inference in connection with estimation and properties of variance of estimators. In this paper, the parameter space of the t-distribution using its Fisher's matrix is de ned. The ${\alpha}$-scalar curvatures to parameter space are calculated.

Estimation of a Bivariate Exponential Distribution with a Location Parameter

  • Hong, Yeon-Ung;Gwon, Yong-Man
    • 한국데이터정보과학회:학술대회논문집
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    • 2002.06a
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    • pp.89-95
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    • 2002
  • This paper considers the problem of estimating paramaters of the bivariate exponential distribution with a loaction parameter for a two-component shared parallel system using component data from system-level life test terminated at the time of the prespecified number of system failure. In the system-level life testing, there are three patterns of failure types; 1) both component failed 2) both component censored 3) one is failed and the other is censored. In the third case, we assume that the failure time might be known or unknown. The maximum likelihood estimators are obtained for the case of known/unknown failure time when the other component is censored.

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