• Title, Summary, Keyword: maximum likelihood estimators

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Statistical Inferences for Bivariare Exponential Distribution in Reliability and Life Testing Problems

  • PARK, BYUNG-GU
    • Journal of the Korean Society for Quality Management
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    • v.13 no.1
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    • pp.31-40
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    • 1985
  • In this paper, statistical estimation of the parameters of the bivariate exponential distribution are studied. Bayes estimators of the parameters are obtained and compared with the maximum likelihood estimators which are introduced by Freund. We know that the method of moments estimators coincide with the maximum likelihood estimators and Bayes estimators are more efficient than the maximum likelihood estimators in moderate samples. The asymptotic distributions of the maximum likelihood estimators and the estimator of mean time to system failure are obtained.

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Estimation for the Half Logistic Distribution under Progressive Type-II Censoring

  • Kang, Suk-Bok;Cho, Young-Seuk;Han, Jun-Tae
    • Communications for Statistical Applications and Methods
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    • v.15 no.6
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    • pp.815-823
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    • 2008
  • In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a half-logistic distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimators of the reliability function using the proposed estimators. We compare the proposed estimators in the sense of the mean squared error.

Estimation for the Half-Triangle Distribution Based on Progressively Type-II Censored Samples

  • Han, Jun-Tae;Kang, Suk-Bok
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.3
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    • pp.951-957
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    • 2008
  • We derive some approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator(MLE) of the scale parameter in the half-triangle distribution based on progressively Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimators of the reliability function using the proposed estimators. We compare the proposed estimators in the sense of the mean squared error.

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On the maximum likelihood estimation for a normal distribution under random censoring

  • Kim, Namhyun
    • Communications for Statistical Applications and Methods
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    • v.25 no.6
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    • pp.647-658
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    • 2018
  • In this paper, we study statistical inferences on the maximum likelihood estimation of a normal distribution when data are randomly censored. Likelihood equations are derived assuming that the censoring distribution does not involve any parameters of interest. The maximum likelihood estimators (MLEs) of the censored normal distribution do not have an explicit form, and it should be solved in an iterative way. We consider a simple method to derive an explicit form of the approximate MLEs with no iterations by expanding the nonlinear parts of the likelihood equations in Taylor series around some suitable points. The points are closely related to Kaplan-Meier estimators. By using the same method, the observed Fisher information is also approximated to obtain asymptotic variances of the estimators. An illustrative example is presented, and a simulation study is conducted to compare the performances of the estimators. In addition to their explicit form, the approximate MLEs are as efficient as the MLEs in terms of variances.

SOME POINT ESTIMATES FOR THE SHAPE PARAMETERS OF EXPONENTIATED-WEIBULL FAMILY

  • Singh Umesh;Gupta Pramod K.;Upadhyay S.K.
    • Journal of the Korean Statistical Society
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    • v.35 no.1
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    • pp.63-77
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    • 2006
  • Maximum product of spacings estimator is proposed in this paper as a competent alternative of maximum likelihood estimator for the parameters of exponentiated-Weibull distribution, which does work even when the maximum likelihood estimator does not exist. In addition, a Bayes type estimator known as generalized maximum likelihood estimator is also obtained for both of the shape parameters of the aforesaid distribution. Though, the closed form solutions for these proposed estimators do not exist yet these can be obtained by simple appropriate numerical techniques. The relative performances of estimators are compared on the basis of their relative risk efficiencies obtained under symmetric and asymmetric losses. An example based on simulated data is considered for illustration.

Reliability Estimation for the Exponential Distribution under Multiply Type-II Censoring

  • Kang, Suk-Bok;Lee, Sang-Ki;Choi, Hui-Taeg
    • 한국데이터정보과학회:학술대회논문집
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    • pp.13-26
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    • 2005
  • In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and location parameter of the exponential distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimator (AMLE) of the reliability function by using the proposed estimators. And then we compare the proposed estimators in the sense of the mean squared error.

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Local Influence of the Quasi-likelihood Estimators in Generalized Linear Models

  • Jung, Kang-Mo
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.229-239
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    • 2007
  • We present a diagnostic method for the quasi-likelihood estimators in generalized linear models. Since these estimators can be usually obtained by iteratively reweighted least squares which are well known to be very sensitive to unusual data, a diagnostic step is indispensable to analysis of data. We extend the local influence approach based on the maximum likelihood function to that on the quasi-likelihood function. Under several perturbation schemes local influence diagnostics are derived. An illustrative example is given and we compare the results provided by local influence and deletion.

On the maximum likelihood estimators for parameters of a Weibull distribution under random censoring

  • Kim, Namhyun
    • Communications for Statistical Applications and Methods
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    • v.23 no.3
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    • pp.241-250
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    • 2016
  • In this paper, we consider statistical inferences on the estimation of the parameters of a Weibull distribution when data are randomly censored. Maximum likelihood estimators (MLEs) and approximate MLEs are derived to estimate the parameters. We consider two cases for the censoring model: the assumption that the censoring distribution does not involve any parameters of interest and a censoring distribution that follows a Weibull distribution. A simulation study is conducted to compare the performances of the estimators. The result shows that the MLEs and the approximate MLEs are similar in terms of biases and mean square errors; in addition, the assumption of the censoring model has a strong influence on the estimation of scale parameter.

Estimation for Exponential Distribution Based on Multiply Type-II Censored Samples

  • Kang, Suk-Bok
    • 한국데이터정보과학회:학술대회논문집
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    • pp.203-210
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    • 2004
  • When the available sample is multiply Type-II censored, the maximum likelihood estimators of the location and the scale parameters of two- parameter exponential distribution do not admit explicitly. In this case, we propose some estimators which are linear functions of the order statistics and also propose some estimators by approximating the likelihood equations appropriately. We compare the proposed estimators by the mean squared errors.

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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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