• 한국경영과학회지
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• 제27권4호
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• pp.185-205
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• 2002

In this study, we consider infinite supply of raw materials and backlogged demands as given two boundary conditions. And we need not make any specific assumptions about the inter-arrival of external demand and service time distributions. Under these situations, the ultimate objective of this study is to prove the variability propagation principle in a pull serial line and is to measure it in terms of the first two moments of the inter-departure process subject to number of cards in each cell. Two preparations are required to achieve this objective : The one is to derive a true lower bound of variance of the inter-departure process. The other is to establish a constrained discrete minimax problem for the no backorder (backlogging) probabilities in each cell. We may get some fundamental results necessary to a completion for the proof through the necessary and sufficient conditions for existence of optimal solution of a constrained discrete minimax problem and the implicit function theorem. finally, we propose a numeric model to measure the variability propagation principle. Numeric examples show the validity and applicability of our study.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.889-898
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• 2002

In decision theoretic estimation, the loss function usually emphasizes precision of estimation. However, one may have interest in goodness of fit of the overall model as well as precision of estimation. From this viewpoint, Zellner(1994) proposed the balanced loss function which takes account of both "goodness of fit" and "precision of estimation". This paper considers estimation of the parameter of a Bernoulli distribution using Zellner's(1994) balanced loss function. It is shown that the sample mean $\overline{X}$, is admissible. More general results, concerning the admissibility of estimators of the form $a\overline{X}+b$ are also presented. Finally, minimax estimators and some numerical results are given at the end of paper,at the end of paper.

• 한국수자원학회논문집
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• 제55권2호
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• pp.159-170
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• 2022

기후변화로 인한 도시유역의 물 관련 재해의 심각성이 증가함에 따라 미래 기후 환경에서 도시 유역의 홍수피해를 줄이는 것은 중요한 문제 중 하나이다. 본 연구는 다기준의사결정기법을 이용하여 미래 기후 환경에서 도시 유역의 홍수 피해 저감 효율을 극대화하기 위해 투수성 포장 시설을 설치하기 위한 지역의 우선순위를 선정한다. 과거에 비해 도시화가 많이 진행된 목감천 유역을 대상유역으로 선정하였으며, 목감천 유역의 27개 소유역을 투수성 포장 시설의 설치 가능지역으로 하였다. 2개의 Shared Socioeconomic Pathway (SSP) 시나리오에 따른 Coupled Model Intercomparison Project 6(CMIP6)의 6개 전지구모형(General Circulation Model, GCM)을 사용하여 연구대상지의 미래 월 강수 자료를 추정했다. 투수성 포장의 우선순위를 결정하기 위한 수량 평가 기준은 Driving force-Pressure-State-Impact-Response (DPSIR) 체계를 토대로 선정하였으며, 평가 기준별 투수성 포장 시설의 평가값은 국가통계자료와 Storm Water Management Model의 모의 값을 사용했다. 최종적으로 Fuzzy TOPSIS 및 Minimax regret 방법을 사용하여 투수성 시설을 설치하기 위한 지역의 우선순위를 선정했다. 결국 우선순위가 높은 지역은 목감천 유역의 상류 유역에 비해 도시화가 많이 진행되었고 인구밀도가 높은 하류유역에 집중되었다.

• Computers and Concrete
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• 제21권4호
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.289-298
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• 1999

We consider a minimax approach to make a design robust to many types or uncertainty arising in reality when dealing with non-normal linear models. We try to build a design to protect against the worst case, i.e. to improve the "efficiency" of the worst situation that can happen. In this paper, we especially deal with the generalized linear model. It is a known fact that the generalized linear model is a universal approach, an extension of the normal linear regression model to cover other distributions. Therefore, the optimal design for the generalized linear model has very similar properties as the normal linear model except that it has some special characteristics. Uncertainties regarding the unknown parameters, link function, and the model structure are discussed. We show that the suggested approach is proven to be highly efficient and useful in practice. In the meantime, a computer algorithm is discussed and a conclusion follows.

• Communications for Statistical Applications and Methods
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• 제27권4호
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• pp.445-458
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• 2020

The least absolute shrinkage and selection operator (LASSO) is a popular method for a high-dimensional regression model. LASSO has high prediction accuracy; however, it also selects many irrelevant variables. In this paper, we consider the moderately clipped LASSO (MCL) for the high-dimensional generalized linear model which is a hybrid method of the LASSO and minimax concave penalty (MCP). The MCL preserves advantages of the LASSO and MCP since it shows high prediction accuracy and successfully selects relevant variables. We prove that the MCL achieves the oracle property under some regularity conditions, even when the number of parameters is larger than the sample size. An efficient algorithm is also provided. Various numerical studies confirm that the MCL can be a better alternative to other competitors.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.

• 한국해안해양공학회지
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• 제8권4호
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• pp.297-304
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• 1996

본 연구는 pade 근사 또는 minimax 근사법으로 파랑진행방향의 허용범위를 확장시핀 포물선형 완경사방정식의 적용성 및 구조물에 의한 회절파의 비선형성을 고찰하는 데 그 목적이 있으며, 이를 위하여 불투과성의 이안제가 설치된 파랑장에 위 모델을 기본방정식으로 하여 수치계산을 수행한 후, 수리모형 실험치(Watanabe and Maruyama, 1984)와 비교ㆍ분석하였다. 그 결과 구조물의 기하학적 차폐경계를 따라 증가된 회절효과 때문에 비선형 모델의 파고치가 선형 모델의 파고치보다 크게 나타나며, 파랑진행 허용범위각을 크게 확장시킨 모델은 파랑진행각이 큰 영역에서는 측방향으로 파랑에너지를 높은 정도로 전과시키나 파수의 근사에 의한 누적된 오차 때문에 전반적으로 파고치가 왜곡되어 나타남을 알 수 있다.

• Journal of the Korean Statistical Society
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• 제24권1호
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• pp.149-160
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• 1995

We consider the problem of optimal adaptive estiamtion of the euclidean parameter vector $\theta$ of the univariate non-linerar autogressive time series model ${X_t}$ which is defined by the following system of stochastic difference equations ; $X_t = \sum^p_{i=1} \theta_i \cdot T_i(X_{t-1})+e_t, t=1, \cdots, n$, where $\theta$ is the unknown parameter vector which descrives the deterministic dynamics of the stochastic process ${X_t}$ and ${e_t}$ is the sequence of white noises with unknown density $f(\cdot)$. Under some general growth conditions on $T_i(\cdot)$ which guarantee ergodicity of the process, we construct a sequence of adaptive estimatros which is locally asymptotic minimax (LAM) efficient and also attains the least possible covariance matrix among all regular estimators for arbitrary symmetric density.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1998년도 추계학술대회논문집:21세기에 대비한 지능형 통합항만관리
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• pp.201-209
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• 1998

The propagation of water waves over irregular bottom bathymetry and around islands involves many process. In this study of numerical model is developed current in water of varying depth. The method used is splitting method and minimax approximation. This numerical method used is Crank-Nicolson scheme. This model is applied to Vincent shoal and compared with laboratory data. The results agreed well with laboratory data. The results agreed well with laboratory data. Current effect is considered in this study. So, the model is used for the estimation of rip current in the slowly varying topography.