• Title/Summary/Keyword: Bivariate random censored data

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Reliability for Series and Parallel Systems in Bivariate Pareto Model : Random Censorship Case

  • Cho, Jang-Sik;Cho, Kil-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.3
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    • pp.461-469
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    • 2003
  • In this paper, we consider the series and parallel system which include two components. We assume that the lifetimes of two components follow the bivariate Pareto model with random censored data. We obtain the estimators and approximated confidence intervals of the reliabilities for series and parallel systems based on maximum likelihood estimator and the relative frequency, respectively. Also we present a numerical example by giving a data set which is generated by computer.

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Large Sample Tests for Independence and Symmetry in the Bivariate Weibull Model under Random Censorship

  • Cho, Jang-Sik;Ko, Jeong-Hwan;Kang, Sang-Kil
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.405-412
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    • 2003
  • In this paper, we consider two components system which the lifetimes have a bivariate weibull distribution with random censored data. Here the censoring time is independent of the lifetimes of the components. We construct large sample tests for independence and symmetry between two-components based on maximum likelihood estimators and the natural estimators. Also we present a numerical study.

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Bayesian multiple comparisons in Freund's bivariate exponential populations with type I censored data

  • Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.3
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    • pp.569-574
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    • 2010
  • We consider two components system which have Freund's bivariate exponential model. In this case, Bayesian multiple comparisons procedure for failure rates is sug-gested in K Freund's bivariate exponential populations. Here we assume that the com-ponents enter the study at random over time and the analysis is carried out at some prespeci ed time. We derive fractional Bayes factor for all comparisons under non- informative priors for the parameters and calculate the posterior probabilities for all hypotheses. And we select a hypotheses which has the highest posterior probability as best model. Finally, we give a numerical examples to illustrate our procedure.

Estimation of P(X

  • Kil Ho Cho;Jang Sik Cho;Young Joon Cha;Jae Man Lee
    • Communications for Statistical Applications and Methods
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    • v.3 no.3
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    • pp.253-261
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    • 1996
  • In this paper, we derive the maximum likelihood estimator of P=P(X

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