• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.457-465
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• 1992

현재 또는 장래에 예견되는 거의 모든 통신서비스를 통합적으로 제공할 수 있는 B-ISDN환경하에서 음성통화와 비디오정보, 데이타들이 각기 다른 bit rate와 서비스 요구조건(통화시간, 질등)를 가지고 전송서비스를 받으려 하기때문에 매우 다양한 서비스들의 조합을 고려하여 교환시스팀을 구현해야 한다. B-ISDN에 적합한 전송기술로서 ATM(Asynchronous Transfer Mode)이 일반적으로 제안되고 있는데 이미 10여종의 독특한 ATM시스팀들이 이론적, 실험적 연구단계를 거쳐 거의 실용화 단계까지 이르렀다고 주장되고 있다. 본 논문에서는 ATM교환시스팀의 설계요건과 비교기준을 제시하여 설계 대자인을 주어진 기술제약하에 최적화 할 수 있는 조건을 제시한다. 이때 우선 기본 스위치의 구조를 단단계로 할 것인가 다단계로 할 것인가에 대한 정량적, 확률적인 비교가 행해지고 특히 이미 많은 ATM스위치에서 채택되고 있는 Banyan형태의 망의 성능분석을 보다 현실에 근접하게 할 수 있는 이산적 마코프체인에 의한 모형과 계산방법이 확립된다. 이를 통해 단위스위치내부에 버퍼의 유무, 버퍼를 두는 위치, 또한 버퍼사이즈에 의한 영향등이 세부적으로 분석된다.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.313-322
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• 2016

Purpose: We introduce ways to employ Markov chain model to evaluate the effect of preventive maintenance process. While the preventive maintenance process decreases the failure rate of each subsystems, it increases the downtime of the system because the system can not work during the maintenance process. The goal of this paper is to introduce ways to analyze this trade-off. Methods: Markov chain models are employed. We derive the availability of the system consisting of N repairable subsystems by the methods under various maintenance policies. Results: To validate our methods, we apply our models to the real maintenance data reports of military truck. The error between the model and the data was about 1%. Conclusion: The models developed in this paper fit real data well. These techniques can be applied to calculate the availability under various preventive maintenance policies.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.77-87
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• 2015

Parrondo paradox is the counter-intuitive phenomenon where two losing games can be combined to win or two winning games can be combined to lose. In this paper, we consider an ensemble of players, one of whom is chosen randomly to play game A' or game B. In game A', the randomly chosen player transfers one unit of his capital to another randomly selected player. In game B, the player plays the history-dependent Parrondo game in which the winning probability of the present trial depends on the results of the last two trials in the past. We show that Parrondo paradox exists in this redistribution model of the history-dependent Parrondo game.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.895-900
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• 2010

As the diverse telecommunication services have been developed, network designers need to prevent congestion which may be caused by properties of timecorrelation and burstiness, and unpredictable statistical fluctuation of traffic streams. This paper considers the leaky bucket scheme with combined control of arrival and token rates, in which the arrival rate and the token generation interval are controlled according to the queue length. By using the embedded Markov chain and the supplementary variable methods, we obtain the queue length distribution as well as the loss probability and the mean waiting time.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.1-13
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• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.377-387
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• 1998

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced latent variables that indicates with component of the Superposition model. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Metropolis algorithms along with Gibbs steps are proposed to preform the Bayesian inference of such models. for model determination, we explored the Pre-quential conditional predictive Ordinate(PCPO) criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions, we consider in this paper Superposition of Musa-Okumoto and Erlang(2) models. A numerical example with simulated dataset is given.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.123-136
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• 2020

It is common to encounter correlated multiple outcomes measured on the same subject in various research fields. In developmental toxicity studies, presence of malformed pups and fetal weight are measured on the pregnant dams exposed to different levels of a toxic substance. Joint analysis of such two outcomes can result in more efficient inferences than separate models for each outcome. Most methods for joint modeling assume a normal distribution as random effects. However, in developmental toxicity studies, the response distributions may change irregularly in location and shape as the level of toxic substance changes, which may not be captured by a normal random effects model. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint model for binary and continuous outcomes. In our model, we incorporate a skewed logit model for the binary outcome to allow the response distributions to have flexibly in both symmetric and asymmetric shapes on the toxic levels. We apply our proposed method to data from a developmental toxicity study of diethylhexyl phthalate.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.627-641
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• 2012

We consider a model estimation and model selection methods for the multi-way contingency table data with non-response or missing values. We also consider hierarchical Bayesian model in order to handle a boundary solution problem that can happen in the maximum likelihood estimation under non-ignorable non-response model and we deal with a model selection method to find the best model for the data. We utilized Bayes factors to handle model selection problem under Bayesian approach. We applied proposed method to the pre-election survey for the 2004 Korean National Assembly race. As a result, we got the non-ignorable non-response model was favored and the variable of voting intention was most suitable.