• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.65-74
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• 1993

Suppose that we have two populations(or systems), say ${\Pi}_{1}\;and\;{\Pi}_{2}$, to be tested. A random sample of size n from each population is taken and the test for each system will be terminated when the first r failures among n random samples are observed. This kind of test is caned the type-II censored (or item-censored) testing without replacement. Under this scheme we consider the problem of estimating the unknown parameters of interests and the reliability for a given time t for each population.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.565-584
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• 1999

the covariance process of two martingales provides a useful tool to capture the dependence structure for paired censored data. in this paper it is applied to modify the variances of weighted logrank tests in order to take account of dependence between paired subjects. In the process of modification a 'variance correction term' is introduced. Some variance estimators based on separate samples are considered together. Performance of the estimators are compared through simulation studies. Several independence tests for bivariate sruvival date are also proposed which are naturally reduced from the weighted logrank tests accomodating dependence structure. Simulation studies are carried out to compare the independence tests. Both the weighted logrank tests and the independence tests are illustrated by an example.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.71-77
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• 1997

S independent sample 0,1,2, $\cdots$, s-1 (or stages 0,1,2, $\cdots$, s-1) are available from the Raleigh population. Procedure for predicting any order statistic in the $(s+1)^{th}$ sample is developed by obtaining the predictive distribution at stage s. Bounds for the sample size at stage S, in order to have the variance at stage S less than that at stage (s-1), are obtained.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.41-48
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• 1997

We construct the empirical Bayes(EB) confidence intervals that attain a specified level of EB coverage for the unknown scale parameter in the Weibull distribution with the known shape parameter under the type II censored data. Our general approach is to use an EB bootstrap samples introduced by Larid and Louis(1987). Also, we compare the coverage probability and the expected interval length for these bootstrap intervals with those of the naive intervals through Monte Carlo simulation.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.76-85
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• 2007

A reliability acceptance sampling plan (RASP) consists of a set of life test procedures and rules for eitheraccepting or rejecting a collection of items based on the sampled lifetime data. Most of the existing RASPs areconcerned with the case where test items are available at the same time. However, as in the early stage ofproduct development, it may be difficult to secure test items at the same time. In such a case, it is inevitable toconduct a life test using sequentially supplied samples.In this paper, it is assumed that test items are sequentially supplied, the lifetimes of test items follow anexponential disthbution, failures are monitored continuously, arrival times of test items are known, and thenumber of test items at each arrival time is given. Under these assumptions, RASPs are developed by deter-mining the test completion time and the critical value for the maximum likelihood estimator of the mean lifetimesuch that the producer and consumer risks are satisfied. Then, the developed plans are compared to thetraditional Type-I censored RASPs in terms of the test completion time. Computational results indicate that thetest completion time of the developed RASP is shorter than that of the traditional Type-I censored plan in mostcases considered. It is also found that the superiority of the developed RASP becomes more prominent as theinter-arrival times of test items increase and/or the total number of test items gets larger.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.765-774
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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 triangular distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation for various progressive censoring schemes.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.227-235
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• 1998

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and hazard rate function based on type-II censored samples from a Rayleigh failure model. Bayes calculations can be implemented easily by means of the Gibbs sampler. A numerical study is provided.

• Journal of Applied Reliability
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• v.12 no.3
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• pp.139-152
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• 2012

Successive and simultaneous sudden death tests are compared with the complete and Type II censored samples in terms of expected test duration and Total Time on Test(TTT) subject to the same number of failures in order to maintain the equal statistical precision under Weibull lifetime distribution with known shape parameter. Also, two sudden death tests under a proposed cost model are discussed and a numerical example is provided to illustrate the use of the proposed cost model.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.225-230
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• 1998

By assuming a singly right cenosred sample, we propose the approximate maximum likelihood estimator (AMLE) of the scale parameter of the p-dimensional Rayleigh distribution. We compare the proposed estimator in ·terms of the mean squared error through Monte Carlo methods.

• Journal of Korean Society for Quality Management
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• v.25 no.4
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• pp.71-78
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• 1997

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and failure rate functions based on type-II censored samples from a Burr type-？ failure time model. The Gibbs sampler a, pp.oach brings considerable conceptual and computational simplicity to the calculation of the posterior marginals and reliability. A numerical study is provided.