• 대한산업공학회지
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• 제38권4호
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• pp.249-253
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• 2012

This paper suggests a numerical method for valuation of American options under the Kou model (double exponential jump diffusion model). The method is based on approximation of underlying asset price using a finite-state, time-homogeneous Markov chain. We examine the effectiveness of the proposed method with simulation results, which are compared with those from the conventional numerical method, the finite difference method for PIDE (partial integro-differential equation).

• Management Science and Financial Engineering
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• 제19권2호
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• pp.37-42
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• 2013

This paper suggests a numerical method for valuation of European and American options under the two L$\acute{e}$vy Processes, Normal Inverse Gaussian Model and the Variance Gamma model. The method is based on approximation of underlying asset price using a finite-state, time-homogeneous Markov chain. We examine the effectiveness of the proposed method with simulation results, which are compared with those from the existing numerical method, the lattice-based method.

• 대한산업공학회지
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• 제42권2호
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• pp.86-95
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• 2016

We propose a new approach to the stochastic version of Lanchester model. Commonly used approach to stochastic Lanchester model is through the Markov-chain method. The Markov-chain approach, however, is not appropriate to high dimensional heterogeneous force case because of large computational cost. In this paper, we propose an approximation method of stochastic Lanchester model. By matching the first and the second moments, the distribution of each unit strength can be approximated with multivariate normal distribution. We evaluate an approximation of discrete Markov-chain model by measuring Kullback-Leibler divergence. We confirmed high accuracy of approximation method, and also the accuracy and low computational cost are maintained under high dimensional heterogeneous force case.

• 응용통계연구
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• 제25권5호
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• pp.837-846
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• 2012

Monte Carlo methods have been used in exact inference for contingency tables for a long time; however, they suffer from ergodicity and the ability to achieve a desired proportion of valid tables. In this paper, we apply the stochastic approximation Monte Carlo(SAMC; Liang et al., 2007) algorithm, as an adaptive Markov chain Monte Carlo, to the exact test of mutual independence in a multiway contingency table. The performance of SAMC has been investigated on real datasets compared to with existing Markov chain Monte Carlo methods. The numerical results are in favor of the new method in terms of the quality of estimates.

• 정보과학회 논문지
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• 제44권7호
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• pp.719-725
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• 2017

약 네트워크의 관리 비용을 줄이고 성능을 향상시키기 위해 ETSI(European Telecommunication Standards Institute)는 클라우드 데이터 센터에서 네트워크 기능(Network Function)을 소프트웨어 형태로 구현할 수 있는 네트워크 기능 가상화(Network Function Virtualization) 개념을 도입했다. 네트워크 기능 가상화 구조 내에서 네트워크 기능을 물리적 노드(예: 범용 서버)에 네트워크 기능을 호스팅하여 실제 리소스를 공유할 수 있다. 네트워크 기능 가상화를 지원하는 네트워크 서비스 제공 업체의 경우, 효율적인 자원 할당 방법을 통해 운영비용(OPEX) 및 자본 비용(CAPEX)를 줄일 수 있다. 이에 본 논문에서는 최적화 방법을 통해 Network Service Chain Embedding 문제를 분석하고 Markov Approximation 프레임워크 기반 최적의 솔루션을 제안한다. 제안사항에 대한 시뮬레이션 결과는 평균 CPU 사용률이 73%, 링크 사용률이 최대 53% 증가함을 보여준다.

• 응용통계연구
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• 제30권1호
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• pp.1-12
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• 2017

많은 확률과정이 Markov 특성을 만족하거나 근사적으로 만족하는 것으로 가정된다. Markov 과정에서 특히 관심을 끄는 것은 최초통과시간이다. 최초통과시간에 대한 연구는 Wald의 축차분석에서 시작하여 근사적 특성에 대한 많은 연구가 되어왔고 컴퓨터의 발달로 통계계산적 방법이 사용되면서 근사적 결과가 참값에 가까운 값을 계산할 수 있게 되었다. 이 논문은 Markov 과정의 예로서 지수가중 이동평균 관리도를 사용할 때 평균런길이를 계산하는 과정과 계산상의 주의점, 문제점 등을 연구하였다. 이 결과는 다른 모든 Markov 과정에 적용될 수 있으며 특히 Markov 연쇄로의 근사는 확률과정의 특성의 연구에 유용하고 계산적 접근을 용이하게 한다.

• 한국경영과학회지
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• 제19권3호
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• pp.93-109
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• 1994

Even though sojourn time distributions are essential information in analyzing queueing networks, there are few methods to compute them accurately in non-product form queueing networks. In this study, we model the location process of a typical customer as a GMPH semi-Markov chain and develop computationally useful formula for the transition function and the first-passage time distribution in the GMPH semi-Markov chain. We use the formula to develop an effcient method for approximating sojourn time distributions in the non-product form queueing networks under quite general situation. We demonstrate its validity through numerical examples.

• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.425-445
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• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

• 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.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제19권4호
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• pp.429-441
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• 2015

We study optimal consumption/investment and life insurance purchase rules for a wage earner with mortality risk under regime-switching financial market conditions, in a continuous time-horizon. We apply the Markov chain approximation method and suggest an efficient algorithm using parallel computing to solve the simultaneous Hamilton-Jaccobi-Bellman equations arising from the optimization problem. We provide numerical results under the utility functions of the constant relative risk aversion type, with which we illustrate the effects of regime switching on the optimal policies by comparing them with those in the absence of regime switching.