• Communications for Statistical Applications and Methods
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• 제19권5호
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• pp.705-720
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• 2012

In this paper, we introduce a hierarchical Bayesian model to simultaneously estimate the thresholds of each 6 cities. It was noted in the literature there was a dramatic increases in the number of deaths if the mean temperature passes a certain value (that we call a threshold). We estimate the difference of mortality before and after the threshold. For the hierarchical Bayesian analysis, some proper prior distribution of parameters and hyper-parameters are assumed. By combining the Gibbs and Metropolis-Hastings algorithm, we constructed a Markov chain Monte Carlo algorithm and the posterior inference was based on the posterior sample. The analysis shows that the estimates of the threshold are located at $25^{\circ}C{\sim}29^{\circ}C$ and the mortality around the threshold changes from -1% to 2~13%.

• International journal of advanced smart convergence
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• 제1권1호
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• pp.14-18
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• 2012

This paper presents a fair MAC protocol based on the CSMA/CA algorithm in visible light communication (VLC) networks. The problem of bandwidth sharing among differentiated priority in VLC networks can be solved by using number of backoff time and backoff exponent parameters with AIFS. The proposed algorithm can achieve fair allocation of the bandwidth resource among differentiated priority. The two dimension Markov chain is assisted for analyzing the proposed mechanism about throughput and delay metrics. Numerical results show that our proposed algorithm improves the fairness among different traffic flows.

• 한국공작기계학회논문집
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• 제13권1호
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• pp.28-34
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• 2004

In this study, new way of evaluating manufacturing cost Is organized and applied. In real manufacturing circumstances, tolerances of parts and assemblies are closely related to the cost. Several researches have been tried to identify the relations and set models. Moreover tolerances have influences on the maintenance of the manufacturing facilities. However Past researches have not considered the processing cost for the failed products. Therefore maintenance costs are represented as stochastic expressions, which include reliability of assembly and facilities. The stochastic nature of the maintenance cost is modeled and solved using Markov chain approach. Results show that this approach gives reliable estimations with remarkable computing time reduction.

• 한국IT서비스학회지
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• 제19권2호
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• pp.37-47
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• 2020

A significant proportion of information subjects experience information security breaches, and the number of reports and counseling cases of personal information infringements is increasing. Increased awareness of the importance of information security has raised interest in the personnel in charge of such tasks. However, hiring excellent new workers and preventing turnovers in information security remain unresolved. In this paper, by modeling the job career path of information security workforce as a Markov chain, we analyze the workforce turnover process and long-term turnover trends by information security jobs, and further analyze the number and duration of turnovers required to engage in specific jobs. The results of this study are expected to be a reference to balancing the supply and demand of information security workers for the government and to ensuring efficient management of the workforce for businesses.

• 품질경영학회지
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• 제30권2호
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• pp.99-117
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• 2002

Variable sampling interval(VSI) and variable sample size(VSS) control charts vary the sampling rate for the next sample depending on the current chart statistic. This paper develops EWMA charts with the VSI and VSS features, and investigates the effectiveness of these charts in context of an economic model. The economic properties of these charts are evaluated by using Markov chain methods. The model contains cost parameters which allow the specification of the costs associated with sampling, false alarms, and operating off target. This economic model can be used to quantify the cost saving that can be obtained by using control charts with the VSI and VSS features instead of with the fixed sampling rate(FSR) feature, and can also be used to gain insight into the way that control charts with the VSI and VSS features should be designed to achieve optimal economic performance. The economic performance of X charts with the VSI and VSS features is also considered.

• Journal of the Korean Statistical Society
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• 제35권3호
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• pp.281-303
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• 2006

In an earlier work, we investigated the problem of using linear programming to bound performance measures in a discrete time Jackson network. There it was assumed that the system evolution is controlled by the early arrival scheme. This assumption implies that the system can't be modelled by a Markov chain. This problem was resolved and performance bounds were calculated. In the present work, we use a modification of the early arrival scheme (without corrupting it) in order to make the system evolves as a Markov chain. This modification enables us to obtain explicit expressions for certain moments that could not be calculated explicitly in the pure early arrival scheme setting. Moreover, this feature implies a reduction in the linear program size as well as the computation time. In addition, we obtained tighter bounds than those appeared before due to the new setting.

• Journal of the Korean Statistical Society
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• 제35권3호
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• pp.239-250
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• 2006

A $2{\times}2$ crossover design including carryover effect is considered for assessment of population bioequivalence of two drug formulations in a Bayesian framework. In classical analysis, it is complex to deal with the carryover effect since the estimate of the drug effect is biased in the presence of a carryover effect. The proposed method in this article uses uninformative priors and vague proper priors for objectiveness of priors and the posterior probability distribution of the parameters of interest is derived with given priors. The posterior probabilities of the hypotheses for assessing population bioequivalence are evaluated based on a Markov chain Monte Carlo simulation method. An example with real data set is given for illustration.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.773-788
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• 2000

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.63-75
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• 2002

Most attempts at Bayesian analysis of neural networks involve hierarchical modeling. We believe that similar results can be obtained with simpler models that require less computational effort, as long as appropriate restrictions are placed on parameters in order to ensure propriety of posterior distributions. In particular, we adopt a model first introduced by Lee (1999) that utilizes an improper prior for all parameters. Straightforward Gibbs sampling is possible, with the exception of the bias parameters, which are embedded in nonlinear sigmoidal functions. In addition to the problems posed by nonlinearity, direct sampling from the posterior distributions of the bias parameters is compounded due to the duplication of hidden nodes, which is a source of multimodality. In this regard, we focus on sampling from the marginal posterior distribution of the bias parameters with Markov chain Monte Carlo methods that combine traditional Metropolis sampling with a slice sampler described by Neal (1997, 2001). The methods are illustrated with data examples that are largely confined to the analysis of nonparametric regression models.

• Communications for Statistical Applications and Methods
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• 제25권4호
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• pp.355-371
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• 2018

The two parameter negative exponential distribution has many practical applications in queuing theory such as the service times of agents in system, the time it takes before your next telephone call, the time until a radioactive practical decays, the distance between mutations on a DNA strand, and the extreme values of annual snowfall or rainfall; consequently, has many applications in reliability systems. This paper considers an estimation problem of stress-strength model with two parameter negative parameter exponential distribution. We introduce a maximum penalized likelihood method, Bayes estimator using Lindley approximation to estimate stress-strength model and compare the proposed estimators with regular maximum likelihood estimator for complete data. We also introduce a maximum penalized likelihood method, Bayes estimator using a Markov chain Mote Carlo technique for incomplete data. A Monte Carlo simulation study is performed to compare stress-strength model estimates. Real data is used as a practical application of the proposed model.