• Title/Summary/Keyword: Exponential Location Parameter

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Reference Priors for the Location Parameter in the Exponential Distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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    • pp.1409-1418
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    • 2008
  • In this paper, we develop the reference priors for the common location parameter in two parameter exponential distributions. We derive the reference priors and prove the propriety of joint posterior distribution under the general prior including the reference priors. Through the simulation study, we show that the proposed reference prior matches the target coverage probabilities in a frequentist sense.

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AMLEs for the Exponential Distribution Based on Multiply Type-II Censored Samples

  • Kang Suk-Bok;Lee Sang-Ki
    • Communications for Statistical Applications and Methods
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    • v.12 no.3
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    • pp.603-613
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    • 2005
  • We propose some estimators of the location parameter and derive the approximate maximum likelihood estimators (AMLEs) of the scale parameter in the exponential distribution based on multiply Type-II censored samples. We calculate the moments for the proposed estimators of the location parameter, and the AMLEs which are the linear functions of the order statistics. We compare the proposed estimators in the sense of the mean squared error (MSE) for various censored samples.

Bayesian Test for the Difference of Exponential Guarantee Time Parameters

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.1095-1106
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    • 2005
  • When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. The reference prior in non-regular exponential model is derived. Under this reference prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.

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Estimation of Gini Index of the Exponential Distribution by Bootstrap Method

  • Kang, Suk-Bok;Cho, Young-Suk
    • Communications for Statistical Applications and Methods
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    • v.3 no.3
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    • pp.291-297
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    • 1996
  • In this paper, we propose the jackknife estimator and the bootstrap estimator of Gini index of the two-parameter exponential distribution when the location parameter $\theta$ is unknown and the scale parameter $\sigma$is known. Sinilarly, we propose the bias location parameter $\theta$ and the scale parameter $\sigma$ are unknown. The bootstrap estimator is more efficient than the other estimators when the location parameter $\theta$is unknown and the scale parameter $\sigma$ is known, and the bias corrected estimator is more efficient than the MLE when both the location parameter $\theta$ and the scale parameter $\sigma$are unknown.

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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.

Bayesian Test for the Difference of Exponential Guarantee Time Parameters

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • 한국데이터정보과학회:학술대회논문집
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    • 2004.04a
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    • pp.15-23
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    • 2004
  • When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. Under the noninformative prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.

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MRE for Exponential Distribution under General Progressive Type-II Censored Samples

  • Kang, Suk-Bok;Cho, Young-Suk
    • Journal of the Korean Data and Information Science Society
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    • v.9 no.1
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    • pp.71-76
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    • 1998
  • By assuming a general progressive Type-II censored sample, we propose the minimum risk estimator (MRE) of the location parameter and the scale parameter of the two-parameter exponential distribution. An example is given to illustrate the methods of estimation discussed in this paper.

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Confidence Intervals and Joint Confidence Regions for the Two-Parameter Exponential Distribution based on Records

  • Asgharzadeh, A.;Abdi, M.
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.103-110
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    • 2011
  • Exponential distribution is widely adopted as a lifetime model. Many authors have considered the interval estimation of the parameters of two-parameter exponential distribution based on complete and censored samples. In this paper, we consider the interval estimation of the location and scale parameters and the joint confidence region of the parameters of two-parameter exponential distribution based on upper records. A simulation study is done for the performance of all proposed confidence intervals and regions. We also propose the predictive intervals of the future records. Finally, a numerical example is given to illustrate the proposed methods.

Estimation for the Double Exponential Distribution Based on Type-II Censored Samples

  • Kang, Suk-Bok;Cho, Young-Suk;Han, Jun-Tae
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.1
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    • pp.115-126
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    • 2005
  • In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and location parameter of the double exponential distribution based on Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

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