• Journal of Korean Institute of Industrial Engineers
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• v.43 no.2
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• pp.147-153
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• 2017

In order to demonstrate a target reliability with a specified confidence level, a new two-stage Bayesian Reliability Demonstration Test (RDT) plans that is known to be more effective than a corresponding single-stage one is proposed and developed by Bayesian framework with beta prior distribution for Weibull life time distribution. A numerical example is provided to illustrate the proposed RDT plans and compared with other non-Bayesian and Bayesian plans. Comparative results show that the proposed Bayesian two-stage plans have some merits in terms of required and expected testing time and probability of acceptance.

• Journal of Applied Reliability
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• v.14 no.4
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• pp.220-224
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• 2014

A Bayesian zero-failure reliability demonstration test method for products with Weibull lifetime distribution is presented. Inverted gamma prior distribution for the scale parameter of the Weibull distribution is used to design the Bayesian test plan and selecting a prior distribution using a prior test information is discussed. A test procedure with zero-failure acceptance criterion is developed that guarantee specified reliability of a product with given confidence level. An example is provided to illustrate the use of the developed Bayesian reliability demonstration test method.

• Journal of Applied Reliability
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• v.13 no.1
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• pp.1-10
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• 2013

A Bayesian zero-failure reliability demonstration test method for products with exponential lifetime distribution is presented. Beta prior distribution for reliability of a product is used to design the Bayesian test plan and selecting a prior distribution using a prior test information is discussed. A test procedure with zero-failure acceptance criterion is developed that guarantees specified reliability of a product with given confidence level. An example is provided to illustrate the use of the developed Bayesian reliability demonstration test method.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.115-127
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• 2017

This paper proposes a Bayesian test for the equivalence of survival functions in multiple groups. Proposed Bayesian test use the model of Cox's regression with time-varying coefficients. B-spline expansions are used for the time-varying coefficients, and the proposed test use only the partial likelihood, which provides easier computations. Various simulations of the proposed test and typical tests such as log-rank and Fleming and Harrington tests were conducted. This result shows that the proposed test is consistent as data size increase. Specifically, the power of the proposed test is high despite the existence of crossing hazards. The proposed test is based on a Bayesian approach, which is more flexible when used in multiple tests. The proposed test can therefore perform various tests simultaneously. Real data analysis of Larynx Cancer Data was conducted to assess applicability.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.67-77
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• 2002

We propose the Bayesian testing for the equality of K-exponential populations means with Type-II censored data. Specially we use the fractional Bayesian factors suggested by O'Hagan (1995) based on the noninformative priors for the parameters. And, we investigate the usefulness of the proposed Bayesian testing procedures via both real data analysis and simulations and compare the classical likelihood ratio(LR) test with the proposed Bayesian test.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.327-333
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• 2006

In this paper, we consider the Bayesian hypotheses testing for independence in bivariate exponential model. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the fractional Bayes factor. Also we give some numerical results to illustrate our results.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1547-1555
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• 2017

This paper studies Bayesian test of homogeneity in contingency tables made by discretizing a continuous variable. Sometimes when we are considering events of interest in small area setup, we can think of discretization approaches about the continuous variable. If we properly discretize the continuous variable, we can find invisible relationships between areas (groups) and a continuous variable of interest. The proper discretization of the continuous variable can support the alternative hypothesis of the homogeneity test in contingency tables even if the null hypothesis was not rejected through k-sample tests involving one-way ANOVA. In other words, the proportions of variables with a particular level can vary from group to group by the discretization. If we discretize the the continuous variable, it can be treated as an analysis of the contingency table. In this case, the chi-squared test is the most commonly employed method. However, further discretization gives rise to more cells in the table. As a result, the count in the cells becomes smaller and the accuracy of the test becomes lower. To prevent this, we can consider the Bayesian approach and apply it to the setup of the homogeneity test.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.677-687
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• 2004

We consider the comparison of two one-parameter exponential distributions with the complete data as well as the type II censored data. We adapt Bayesian test procedure for nested hypothesis based on the Bayesian reference criterion. Specifically we derive the expression for the Bayesian reference criterion to solve our problem. Also we provide numerical examples using simulated data sets to illustrate our results.

• Journal of Applied Reliability
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• v.14 no.3
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• pp.162-168
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• 2014

This paper discusses the application of Bayesian techniques with test data on similar products for performing the Economical Reliability Test of new one-shot device. Using the test data on similar products, reliability test required lower sample size currently being spent in order to demonstrate a target reliability with a specified confidence level. Furthermore, lower sample size reduces cost, time and various resources on reliability test. In this paper, we use similarity as calculating weight of similar products and analyze similarity between new and similar product for comparison of the essential function.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.97-111
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• 1998

This paper suggests a Bayesian method for testing first-order markov correlation among linear regression disturbances. As a Bayesian test criterion, Bayes factor is derived in the form of generalized Savage-Dickey density ratio that is easily estimated by means of posterior simulation via Gibbs sampling scheme. Performance of the Bayesian test is evaluated and examined based upon a Monte Carlo experiment and an empirical data analysis. Efficiency of the posterior simulation is also examined.