• Journal of the Chungcheong Mathematical Society
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• v.3 no.1
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• pp.97-101
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• 1990

For testing a hypothesis $H:{\theta}={\theta}_1$, vs $A:{\theta}={\theta}_2$ (${\theta}_1$ < ${\theta}_2$, we obtain a truncated sequential bayes procedure which minimizes the average sample size between ${\theta}_1$ and ${\theta}_2$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.806-809
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• 1993

This paper focuses on the usage of the fuzzy set theory in decision making systems. The approach to calculation of generalized membership function, based on application of method of principal components is proposed. For solving of the problem of fuzzy forecasting the development of Bayes procedure is used. The structure of decision making system, in which following procedures are fulfilled, is discussed.

• Journal of Korean Society for Quality Management
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• v.34 no.1
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• pp.73-77
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• 2006

In this paper, the Bayesian procedure for the multiple test of fraction nonconforming, p, is proposed. It is the procedure for checking whether the process is out of control, in control, or under the permissible level for p. The procedure is as follows: first, setting up three types of models, $M_1:p=p_0,\;M_2:pp_0$, second, computing the posterior probability of each model. and then choosing the model with the largest posterior probability as a model most fitted for the observed sample among three competitive models. Finally, the simulation study is performed to examine the proposed method.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.325-329
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• 2006

In this paper, the Bayesian procedure for the multiple test of fraction nonconforming, p, is proposed. It is the procedure for checking whether the process is out of control, in control, or under the permissible level for p. The procedure is as follows: first, setting up three types of models, $M_1:p=p_0,\;M_2:pp_0$, second, computing the posterior probability of each model. and then choosing the model with the largest posterior probability as a model most fitted for the observed sample among three competitive models. Finally, the simulation study is performed to examine the proposed method.

• International Journal of Reliability and Applications
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• v.18 no.2
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• pp.83-98
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• 2017

Stress-strength reliability problems arise frequently in applied statistics and related fields. In the context of reliability, the stress-strength model describes the life of a component, which has a random strength X and is subjected to random stress Y. The component fails at the instant that the stress applied to it exceeds the strength and the component will function satisfactorily whenever X > Y. The problem of estimation the reliability parameter in a stress-strength model R = P[Y < X], when X and Y are two independent two-parameter Lindley random variables is considered in this paper. The maximum likelihood estimator (MLE) and Bayes estimator of R are obtained. Also, different confidence intervals of R are obtained. Simulation study is performed to compare the different proposed estimation methods. Example in real data is used as practical application of the proposed procedure.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.271-288
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• 2003

In this paper, when the variance of the normal distribution is related to the mean, we develop noninformative priors such as matching priors and reference priors. We prove that the second order matching prior matches alternative coverage probabilities up to the same order and also it is a HPD matching prior. It turns out that one-at-a-time reference prior satisfies a second order matching criterion. Then using these noninformative priors, we develop a Bayesian test procedure for the equality of the means and variances of two independent normal distributions using fractional Bayes factor. Some simulation study is performed, and a real data example is also provided.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.417-426
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• 2001

This paper addresses the nonparametric Bayesian estimation for the exponential populations under type II censoring. The Dirichlet process prior is used to provide nonparametric Bayesian estimates of parameters of exponential populations. In the past, there have been computational difficulties with nonparametric Bayesian problems. This paper solves these difficulties by a Gibbs sampler algorithm. This procedure is applied to a real example and is compared with a classical estimator.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.443-461
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• 2009

In order to overcome the lack of Korean credit rating migration data, we consider an empirical Bayes procedure to estimate credit rating migration matrices. We derive the posterior probabilities of Korean credit rating transitions by utilizing the Moody's rating migration data and the credit rating assignments from Korean rating agency as prior information and likelihood, respectively. Metrics based upon the average transition probability are developed to characterize the migration matrices and compare our Bayesian migration matrices with some given matrices. Time series data for the metrics show that our Bayesian matrices are stable, while the matrices based on Korean data have large variation in time. The bootstrap tests demonstrate that the results from the three estimation methods are significantly different and the Bayesian matrices are more affected by Korean data than the Moody's data. Finally, Monte Carlo simulations for computing the values of a portfolio and its credit VaRs are performed to compare these migration matrices.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1149-1160
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• 2011

Model selection and hypothesis testing problems in Bayesian inference are still debated between scholars. Bayesian factors traditionally used as a criterion in Bayesian hypothesis testing and model selection, are easy to understand but sometimes hard to compute. In addition, there are other model selection criterions such as DIC(Deviance Information Criterion) by Spiegelhalter et al. (2002) and Bayesian P-values for testing. In this paper, we briefly introduce the Bayesian hypothesis testing and model selection procedure. In addition we have applied a Bayesian inference to Swiss banknote data by a fitting logistic regression model and computing several test statistics to see if they provide consistent results.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.77-87
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• 2009

Bayesian methods have been focused in recent years for solving small area estimation problems. In this paper, the hierarchical Bayes procedure is implemented via MCMC techniques and compared with the results of One-way, GLM-Normal, and GLM-Gamma cases by analyzing real data of insurance benefit for customer segmentations. After analyzing insurance benefit real data for customer segmentations, we can conclude that the insurance benefit estimator through the small area estimation is more efficient than the estimators by other methods. In addition, we found that the small area estimation gave accurate estimation result for the small number domains.