• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1057-1068
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• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.188-196
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• 2017

Purpose: The purpose of this research is to determine optimal replacement age using non-informative prior information and Bayesian method. Methods: We propose a novel approach using Bayesian method to determine the optimal replacement age in block replacement policy by defining the prior probability with data on failure time and repair time. The Marcov Chain Monte Carlo simulation is used to investigate the asymptotic distribution of posterior parameters. Results: An optimal replacement age of block replacement policy is determined which minimizes cost and nonoperating time when no information on prior distribution of parameters is given. Conclusion: We find the posterior distribution of parameters when lack of information on prior distribution, so that the optimal replacement age which minimizes the total cost and maximizes the total values is determined.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.725-732
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• 2002

P-values are often perceived as measurements of degree of compatibility between the current data and the hypothesized model. In this paper, a new concept of Bayesian p-values is proposed and studied under the non-informative prior distributions, which can be thought as the Bayesian counterparts of the classical p-values in the sense of using the concept of significance level. The performances of the proposed Bayesian p-values are compared with those of the classical p-values through several examples.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.787-795
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• 2001

In this paper, Bayesian hypothesis testing procedures are proposed under the non-informative prior distributions, which can be thought as the Bayesian counterparts of the classical ones in the sense of using the concept of significance level. The performances of proposed procedures are compared with those of classical procedures through several examples.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.221-227
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• 2002

The concept of pivot has been widely used in various classical inferences. In this paper, it is proved by use of pivotal quantities that the Bayesian inferences can be arrived at the same results of classical inferences for the location-scale parameters models under the assumption of non-informative prior distributions. Some theorems are proposed in which the posterior distribution and the sampling distribution of a pivotal quantity coincide. The theorems are applied illustratively to some statistical models.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.413-430
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• 2020

The multiply Type-II hybrid censoring scheme is disadvantaged by an experiment time that is too long. To overcome this limitation, we propose a generalized multiply Type-II hybrid censoring scheme. Some estimators of the scale parameter of the exponential distribution are derived under a generalized multiply Type-II hybrid censoring scheme. First, the maximum likelihood estimator of the scale parameter of the exponential distribution is obtained under the proposed censoring scheme. Second, we obtain the Bayes estimators under different loss functions with a noninformative prior and an informative prior. We approximate the Bayes estimators by Lindleys approximation and the Tierney-Kadane method since the posterior distributions obtained by the two priors are complicated. In addition, the Bayes estimators are obtained by using the Markov Chain Monte Carlo samples. Finally, all proposed estimators are compared in the sense of the mean squared error through the Monte Carlo simulation and applied to real data.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3225-3234
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• 2022

Alloy 690 tubing has been shown to be highly resistant to primary water stress corrosion cracking (PWSCC). Nevertheless, predicting the failure by PWSCC in Alloy 690 SG tubes is indispensable. In this work, a Bayesian-based statistical approach is proposed to predict the occurrence of failure by PWSCC in Alloy 690 SG tubing. The prior distributions of the model parameters are developed based on the prior knowledge or information regarding the parameters. Since Alloy 690 is a replacement for Alloy 600, the parameter distributions of Alloy 600 tubing are used to gain prior information about the parameters of Alloy 690 tubing. In addition to estimating the model parameters, analysis of tubing reliability is also performed. Since no PWSCC has been observed in Alloy 690 tubing, only right-censored free-failure life of the tubing are available. Apparently the inference is sensitive to the choice of prior distribution when only right-censored data exist. Thus, one must be careful in choosing the prior distributions for the model parameters. It is found that the use of non-informative prior distribution yields unsatisfactory results, and strongly informative prior distribution will greatly influence the inference, especially when it is considerably optimistic relative to the observed data.

• Journal of Korea Water Resources Association
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• v.46 no.3
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• pp.315-326
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• 2013

Despite of the importance on the maintenance of a reservoir storage, relatively few studies have addressed the stochastic reliability analysis including uncertainty on the decrease of the reservoir storage by the sedimentation. Therefore, the stochastic gamma process under the reliability framework is developed and applied to estimate the reduction of the Soyang Dam reservoir storage in this paper. Especially, in the estimation of parameters of the stochastic gamma process, the Bayesian MCMC scheme using informative prior distribution is used to incorporate a wide variety of information related with the sedimentation. The results show that the selected informative prior distribution is reasonable because the uncertainty of the posterior distribution is reduced considerably compared to that of the prior distribution. Also, the range of the expected life time of the dead storage in Soyang Dam reservoir including uncertainty is estimated from 119.3 years to 183.5 years at 5% significance level. Finally, it is suggested that the improvement of the assessment strategy in this study can provide the valuable information to the decision makers who are in charge of the maintenance of a reservoir.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.797-803
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• 2001

This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.