• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.19-27
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• 1994

It is known that the maximum likelihood method does not provide explicit estimator for the scale parameter of the Weibull distribution based on Type-II censored samples. In this paper we provide an approximate maximum likelihood estimator (AMLE) of the scale parameter of the Weibull distribution with Type-II censoring. We obtain the asymptotic variance and simulate the values of the bias and the variance of this estimator based on 3000 Monte Carlo runs for n = 10(10)30 and r,s = 0(1)4. We also simulate the absolute biases of the MLE and the proposed AMLE for complete samples. It is found that the absolute bias of the AMLE is smaller than the absolute bias of the MLE.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.961-969
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• 2009

A hybrid censoring is a mixture of Type-I and Type-II censoring schemes. This paper deals with estimation based on Type-I hybrid censored samples from the half triangle distribution. We derive some estimators of the scale parameter of the half triangle distribution based on Type-I hybrid censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.6
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• pp.67-74
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• 1995

This paper proposes the step-stress type-I censoring model for analyzing the data of accelerated life test and reducing the time of accelerated life test. In order to obtain the data of accelerated life test, the step-stress accelerated life test was run with voltage stress to CMOS Hex Buffer. The Weibull distribution, the Inverse-power-law model and Maximum likelihood method were used. The iterative procedure using modified-quasi-linearization method is applied to solve the nonlinear equation. The proposed Weibull step-stress type-I censoring model exactly estimases the life time of units, while reducting the time of accelerated life test and the equipments of test.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.603-613
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• 2011

Exponentiated distribution has been used in reliability and survival analysis especially when the data is censored. In this paper, we derive Bayesian estimation of the shape parameter, reliability function and failure rate function in the exponentiated distribution family based on Type-II right censored data. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, the mean square errors of the estimates are computed. Comparisons are made between these estimators using Monte Carlo simulation study.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.225-233
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• 2000

In life-testing experiments, in which the longest time an experimental unit is on test is not a failure time, but rather a censored observation. For the situation the Kaplan-Meier estimator is known to be a baised estimator of the survival function. Several modifications of the Kaplan-Meier estimator are examined and compared with bias and mean squared error.

• Journal of the Korean Data and Information Science Society
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• v.2
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• pp.23-39
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• 1991

The maximum likelihood estimators and Bayes estimators of the parameters and reliability function for the two-parameter Weibull distribution under the type-II progressively censoring schemes are derived when a shape parameter is known and unknown, respectively. Efficiencies for above estimators are also compared each other in terms of the mean square errors, and Bayes risk sensitivities of the Bayes estimators are investigated.

• Journal of the Korean Data and Information Science Society
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• v.2
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• pp.45-62
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• 1991

In this paper we propose new nonparametric estimators of the survival function using spline function under the progressively random censoring scheme. This sampling scheme is applied in many practical situations such as clinical trials or the life testing problems. We also investigate the behaviors for some estimators in the proposed class and the performance of progressively random censoring scheme through the numerical examples and Monte Carlo simulation.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2747-2757
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• 2018

We consider the MLE (maximum likelihood estimate) and Bayesian estimates of three-parameter bathtub-shaped lifetime distribution based on the progressive type II censoring with binomial removal. Jung, Chung (2018) proposed the three-parameter bathtub-shaped distribution which is the extension of the two-parameter bathtub-shaped distribution given by Zhang (2004). Jung, Chung (2018) investigated its properties and estimations. The maximum likelihood estimates are computed using Newton-Raphson algorithm. Also, Bayesian estimates are obtained under the balanced loss function using MCMC (Markov chain Monte Carlo) method. In particular, BSEL (balanced squared error loss) function is considered as a special form of balanced loss function given by Zellner (1994). For comparing theirs MLEs with the corresponding Bayes estimates, some simulations are performed. It shows that Bayes estimates is better than MLEs in terms of risks. Finally, concluding remarks are mentioned.

• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.480-486
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• 2005

This paper proposes a method to consider an aging failure probability and survival probability of power system components, though only aging failure probability has been considered in existing mean life calculation. The estimates of the mean and its standard deviation is calculated by using Weibull distribution, and each estimated parameters is obtained from Data Analytic Method (Type H Censoring). The parameter estimation using Data Analytic Method is simpler and faster than the traditional calculation method using gradient descent algorithm. This paper shows calculation procedure of the mean life and its standard deviation by the proposed method and illustrates that the estimated results are close enough to real historical data of combustion turbine generating units in Korean systems. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an are-related reliability index.