• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• 한국디지털정책학회:학술대회논문집
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• 2006.06a
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• pp.87-109
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• 2006

The purpose of this paper is to examine the present performance evaluation methods and to make Monte Carlo Simulation Model for the IT-based Government innovation project. It is suggested the proper ways in applying of Monte Carlo Simulation Model by integration of present evaluation methods. It develops the theoretical framework for this paper, examining the existing literature on proposing an approach to the key concepts of the economic impact analysis methods. It examines the actual conditions of performance evaluation focusing on the It-based Government Innovation project. It considers how the simulation model is applied to the performance management in the public innovation project focusing on the framework, process and procedure of performance management.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.115-128
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• 2002

Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.1-7
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• 1997

본 논문에서는 해얀구조물의 신뢰성을 평가할 수 있는 새로운 방법을 제시하였다. 우선 구조의 저항능력에 대한 응답면을 구축하였고, Person 곡선중 하나를 이용해서 응답면을 근사 시킨후 Monte-Carlo Simulation을 수행하였고, 최종적으로 수치적분법을 적용해서 파괴확률을 구하였다. 해양구조물에의 적용을 통해 본 논문의 방법이 갖는 정당성을 보였다.

• Korean Chemical Engineering Research
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• v.46 no.4
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• pp.792-798
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• 2008

Supply chain management (SCM) has been drawn increasing attention in industries and academia. The attention is mainly due to a need to integrate the multiple activities in a process network from the overall perspective under the constantly varying economic environment. While many researchers have been addressing various issues of SCM, there is not much research explicitly handling the overall dynamics of supply chain entities from PSE literature. In this two-part series paper, it is investigated how the overall supply chain processing times vary in response to the variation of individual entities using Monte Carlo simulation. Instead of figuring out the operation levels of individual entities, the overall operation time called TAT(Turn-Around-Time) is proposed as a performance indicator. An example of 7 entity-supply chain is presented to illustrate the proposed methodology.

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.661-671
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• 2003

The reliability model is developed for analyzing parameter uncertainty and estimating of flood inundation characteristics in a protected lowland. The approach is based on the concept of levee safety factor and the statistical analysis of model parameters affecting the variability of flood levels. Monte Carlo simulation is incorporated into the varied flow and unsteady flow analysis to quantify the impact of parameter uncertainty on the variability of flood levels. The model is applied to a main stem of the Nakdong River from Hyunpoong to Juckpogyo station. Simulation results show that the characteristics of channel overflow and return now are well simulated and the mass conservation was satisfied. The inundation depth and area are estimated by taking into consideration of the uncertainty of width and duration time of levee failure.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.957-962
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• 2008

We propose a model for the line expansion problem in the AMOLED (Active Matrix Organic Light Emitting Diodes) industry, which now faces market uncertainty: for example, changing customer needs, technological development path, etc. We focus on the optimal investment time and size of the AMOLED production lines. In particular, employed here is the ROV (Real Options Valuation) model to show how to capture the value of line expansion and to determine the optimal investment time. The ROV framework provides a systematic procedure to quantify an expected outcome of a flexible decision which is not possible in the frame of the traditional NPV (Net Present Value) approach. Furthermore, we also use Monte Carlo simulation to measure the uncertainty associated with the line expansion decision; Monte Carlo simulation estimates the volatility of a decision alternative. Lastly, we present a scenario planning to be conducted for what-if analysis of the ROV model.

• Journal of Astronomy and Space Sciences
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• v.15 no.2
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• pp.417-425
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• 1998

Monte Carlo analysis is performed for the first acquisition and tracking of the KOMP-SAT spacecrat in GSOC tracking station after separation from Taurus launch vehicle. The error bounds in position and velocity vector in Earth-fixed coordinate system at injection point are assumed based on the previous launch mission. Ten thousands injection orbital elements with normal distribution are generated and propagated for Monte Carlo analysis. The tracking antenna pointing errors at spacecraft rising time and closest approach time at German Space Operations Center(GSOC) Weiheim track-ing station are derived. Then the tracking antenna scanning angles are analyzed for acquisition and tracking of the KOMPSAT signal.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.71-94
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• 2009

In this paper a procedure for Monte Carlo simulation of univariate stationary stochastic processes with the aid of neural networks is presented. Neural networks operate model-free and, thus, circumvent the need of specifying a priori statistical properties of the process, as needed traditionally. This is particularly advantageous when only limited data are available. A neural network can capture the "pattern" of a short observed time series. Afterwards, it can directly generate stochastic process realizations which capture the properties of the underlying data. In the present study a simple feed-forward network with focused time-memory is utilized. The proposed procedure is demonstrated by examples of Monte Carlo simulation, by synthesis of future values of an initially short single process record.