• Journal of Korean Society for Quality Management
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• v.31 no.4
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• pp.194-202
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• 2003

In this paper, we propose goodness of fit test statistics for exponentiality in accelerated life tests data based on Kullback­Leibler information functions. This acceleration model is assumed to be a tampered random variable model. The procedure is applicable when the exponential parameter using the data from accelerated life tests is or is not specified under null hypothesis. And we compare the power of the proposed test statistics with Kolmogorov­Smirnov, Cramer von Mises and Anderson­Darling statistics in the small sample.

• Journal of the Korean Statistical Society
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• v.18 no.2
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• pp.125-134
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• 1989

This paper presents optimum simple step-stress accelerated life test plans for the case where the test process is observed periodically at intervals of the same length. Two types of failure data, periodically observed complete data and periodically observed censored data, are considered. An exponential life distribution with a mean that is a log-linear function of stress, and a cumulative exposure model for the effect of changing stress are assumed. For each type of data, the optimum test plan which minimizes the asymptotic variance of the maximum likelihood estimator of the mean life at a design stress is obtained and its behaviors are studied.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.563-573
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• 2000

In this paper, we propose three estimators of Kullback-Leibler Information functions using the data from accelerated life tesb. This acceleration model is assumed to be a tampered random variable model. Some asymptotic properties of proposed estimators are proved. Simulations are performed for comparing the small sample properties of the proposed estimators under use condition of accelerated life test.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.87-94
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• 1994

In this paper, we consider optimum plan to determine stress change times under the three-step stress PALTs, assuming that each test units follows an exponential distribution. The tampered random variable(TRV) model for the three-step stress PALTs setup are introduced, and maximum likelihood estimators(MLEs) of the failure rate and the acceleration factors are obtained. The change times to minimize the generalized asymptotic variance(GAVR) of MLEs of the failure rate and the acceleration factors are proposed for the three-step stress PALTs.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.601-614
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• 2009

This paper derives noninformative priors for scale parameter of exponential distribution when the data are collected in multiple step stress accelerated life tests. We nd the objective priors for this model and show that the reference prior satisfies first order matching criterion. Also, we show that there exists no second order matching prior. Some simulation results are given and using artificial data, we perform Bayesian analysis for proposed priors.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.75-85
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• 1994

In this paper, I introduce the goodness-of-fit test statistics for exponential distribution using accelerated life test data. The ALT lifetime data were obtained by assuming step-stress ALT model, specially TRV model introduced by DeGroot and Goel(1979). The critical values are obtained for proposed test statistics, Kolmogorov-Smirnov, Kuiper, Watson, Cramer-von Mises, Anderson-Darling type, under various sample sizes and significance levels. The powers of the five test statistic are compared through Monte-Cairo simulation technique.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.14-28
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• 1996

We consider optimal designs of partially accelerated life testing which is deviced for parallel systems with the considerably long life time. In partially step-stress life testing, test items are first run simultaneously at use condition for a specified time, and the surviving items are then run at accelerated condition until a predetermined censoring time. In partially constant-stress life testing, test items are run at either use or accelerated condition only until a specified censoring time. The optimal criterion for each test is to minimize either the generalized asymptotic variance of maximum likelihood(ML) estimators of the hazard rates at use condition and the acceleration factors or the asymptotic variance of the ML estimators of the acceleration factors.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1169-1175
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• 2009

In this paper, the inferences of data obtained from periodic inspection and type I censoring for the step-stress accelerated life test are studied. The exponential distribution with a failure rate function that a log-linear function of stress and the tampered failure rate model are considered. The maximum likelihood estimators of the model parameters are estimated and also the optimal stress change time which minimize the asymptotic variance of maximum likelihood estimators of parameters is determined. A numerical example will be given to illustrate the proposed inferential procedures and the sensitivity of the asymptotic variance of the estimated mean by the guessed parameters is investigated.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.121-132
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• 1995

In this paper we consider optimal designs of partially step-stress life testing which is deviced for k-component series systems with the considerably long life time. Test items are first run simultaneously at use condition for a specified time, and the surviving items are then run at accelerated condition until a predetermined censoring time. The optimal criterion for the change time to accelerated condition is to minimized either the generalized asymptotic variance of maximum likelihood estimators of the hazard rates at use condition and the acceleration factors or the asymptotic variance of the maximum likelihood estimators of the acceleration factors.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.37-46
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• 1996

In this paper, we introduce the goodness of fit test procedure for lifetime distribution using step stress accelerated lifetime data. Using the nonpapametric estimate of acceleration factor, we prove the strong consistence of empirical distribution function under null hypothesis. The critical vailues of Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises statistics are computed when the lifetime distibution is assumed to be exponential and Weibull. The power of test statistics are compared through Monte-Cairo simulation study.