• Title/Summary/Keyword: maximum-likelihood

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Estimation for Two-Parameter Rayleigh Distribution Based on Multiply Type-II Censored Sample

  • Han, Jun-Tae;Kang, Suk-Bok
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1319-1328
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    • 2006
  • For multiply Type-II censored samples from two-parameter Rayleigh distribution, the maximum likelihood method does not admit explicit solutions. In this case, we propose some explicit estimators of the location and scale parameters in the Rayleigh distribution by the approximate maximum likelihood methods. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

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Maximum Likelihood Estimator in Two Inverse Gaussian Populatoins with Unknown Common Coefficient of Variation

  • Park, Byungjin;Kim, Keeyoung
    • Journal of the Korean Statistical Society
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    • v.30 no.1
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    • pp.99-113
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    • 2001
  • This paper deals with the problem of estimating the means in two inverse Gaussian populations with equal but unknown coefficient of variation. The maximum likelihood estimators are derived by solving a cubic equation and their asymptotic variances are presented for comparative purpose. Monte-Carlo simulation is conducted to investigate the efficiency of the estimators relative to the sample means over a wide range of values for the sample size and the coefficient of variation. The effect on this efficiency under the departure from the assumption of common coefficient of variation is also studied.

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AMLE for the Gamma Distribution under the Type-I censored sample

  • Kang, Suk-Bok;Lee, Hwa-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.11 no.1
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    • pp.57-64
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    • 2000
  • By assuming a Type-I censored sample, we propose the approximate maximum likelihood estimators(AMLE) of the scale and location parameters of the gamma distribution. We compare the proposed estimators with the maximum likelihood estimators(MLE) in the sense of the mean squared errors(MSE) through Monte Carlo method.

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Estimation in an Exponentiated Half Logistic Distribution under Progressively Type-II Censoring

  • Kang, Suk-Bok;Seo, Jung-In
    • Communications for Statistical Applications and Methods
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    • v.18 no.5
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    • pp.657-666
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    • 2011
  • In this paper, we derive the maximum likelihood estimator(MLE) and some approximate maximum likelihood estimators(AMLEs) of the scale parameter in an exponentiated half logistic distribution based on progressively Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error(MSE) through a Monte Carlo simulation for various censoring schemes. We also obtain the AMLEs of the reliability function.

Goodness-of-fit Test for the Weibull Distribution Based on Multiply Type-II Censored Samples

  • Kang, Suk-Bok;Han, Jun-Tae
    • Communications for Statistical Applications and Methods
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    • v.16 no.2
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    • pp.349-361
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    • 2009
  • In this paper, we derive the approximate maximum likelihood estimators of the shape parameter and the scale parameter in a Weibull distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We develop three modified empirical distribution function type tests for the Weibull distribution based on multiply Type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

A Comparative Study Of Maximum Likelihood Method With Bayesian Approach In Statistical Parameter Estimation Of Static Systems (정적계통의 통계적 퍼래미터 추정에 있어 최우도법과 Bayes식방법과의 비교연구)

  • 한만춘;최경삼
    • 전기의세계
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    • v.22 no.2
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    • pp.51-56
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    • 1973
  • The comparative study of maximum likelihood estimation with Bayesian approach was made by statistical & computational methods in center of a priori information of static systems and the effect of a priori information on the accuracy of the estimatiion was also analyzed. Through the numerical computations of some examples by digital computer, we concluded that maximum likelihood method is better than Bayesian estimation except for almost certain a priori informations. The study may therefore contribute in identification problems of dynamical systems connected with a priori informations.

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Parameter Estimation for an Infinite Dimensional Stochastic Differential Equation

  • Kim, Yoon-Tae
    • Journal of the Korean Statistical Society
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    • v.25 no.2
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    • pp.161-173
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    • 1996
  • When we deal with a Hilbert space-valued Stochastic Differential Equation (SDE) (or Stochastic Partial Differential Equation (SPDE)), depending on some unknown parameters, the solution usually has a Fourier series expansion. In this situation we consider the maximum likelihood method for the statistical estimation problem and derive the asymptotic properties (consistency and normality) of the Maximum Likelihood Estimator (MLE).

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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.

Maximum Likelihood Training and Adaptation of Embedded Speech Recognizers for Mobile Environments

  • Cho, Young-Kyu;Yook, Dong-Suk
    • ETRI Journal
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    • v.32 no.1
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    • pp.160-162
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    • 2010
  • For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.