• 한국항공우주학회지
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• 제39권4호
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• pp.314-320
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• 2011

본 연구에서는 2단으로 구성된 달착륙선 충격 흡수 장치에 대한 최적화를 수행하였다. 충격 흡수 장치의 복잡한 충격거동을 모사하기 위해 1차원 구성방정식 모델을 제안하였으며, 이와 함께 상용해석 소프트웨어인 ABAQUS를 활용하여 최적화를 위한 2차 다항회귀 메타모델을 구성하였다. 구성된 메타모델을 순차적 근사 최적설계 기법에 적용하여 2단 충격 흡수 장치의 최적화를 수행하였으며, 이를 통해 허니컴 구조를 이용한 충격 흡수장치의 셀크기와 포일 두께를 변화시킴에 따라 달착륙선의 월면 착륙 시 충격하중을 크게 저감시킬 수 있음을 확인하였다.

• 응용통계연구
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• 제24권4호
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• pp.709-719
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• 2011

We investigate the latent stage-sequential patterns of drinking behaviors of U.S. adolescents who have started to drink by age 14 years (seven years before the legal drinking age). A multiple-group latent transition analysis(LTA) with logistic regression is employed to identify the subsequent patterns of drinking behaviors among early-onset drinkers. A sample of 1407 early-onset adolescents from the National Longitudinal Survey of Youth(NLSY97) is analyzed using maximum-likelihood estimation. The analysis demonstrates that early-onset adolescents' drinking behaviors can be represented by four latent classes and their prevalence and transition are influenced by demographic factors of gender, age, and race.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.271-284
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• 2002

In this paper we consider the robust inference for the parameter of linear regression model based on weighted least squares. First we consider the sequential test of multiple outliers. Next we suggest the way to assign a weight to each observation $(x_i,\;y_i)$ and recommend the robust inference for linear model. Finally, to check the performance of confidence interval for the slope using proposed method, we conducted a Monte Carlo simulation and presented some numerical results and examples.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.575-582
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• 2019

The problem of selecting variables in the presence of outliers is considered. Variable selection and outlier detection are not separable problems because each observation affects the fitted regression equation differently and has a different influence on each variable. We suggest a simultaneous method for variable selection and outlier detection in a linear regression model. The suggested procedure uses a sequential method to detect outliers and uses all possible subset regressions for model selections. A simplified version of the procedure is also proposed to reduce the computational burden. The procedures are compared to other variable selection methods using real data sets known to contain outliers. Examples show that the proposed procedures are effective and superior to robust algorithms in selecting the best model.

• 품질경영학회지
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• 제40권4호
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• pp.431-440
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• 2012

Purpose: In this paper we propose modified Notz designs which are useful to experimenters who want to adopt the sequential experimentation strategy and to estimate second-order regression model with as few experimental points as possible. Methods: We first present the design matrices and design points in two or three dimensional spaces for such small sized second-order designs as small composite, hybrid, and Notz designs. Modified Notz designs are proposed and compared with some response surface designs in terms of the total number of experimental points, the estimation capability criteria such as D- and A-optimality. Results: When sequential experimentation is necessary, the modified Notz designs are recommendable. Conclusion: The result of this paper will be beneficial to experimenters who need to do experiments more efficiently, especially for those who want to reduce the time of experimentation as much as possible to develop cutting-edge products.

• Asian-Australasian Journal of Animal Sciences
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• 제16권6호
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• pp.894-902
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• 2003

Prebiotics (peptides, N-acetyglucoamine, fructo-oligosaccharides, isomalto-oligosaccharides and galactooligosaccharides) were added to skim milk in order to improve the growth rate of contained Lactobacillus acidophilus, Lactobacillus casei, Bifidobacterium longum and Bifidobacterium bifidum. The purpose of this research was to study the potential synergy between probiotics and prebiotics when present in milk, and to apply modern optimization techniques to obtain optimal design and performance for the growth rate of the probiotics using a response surface-modeling technique. To carry out response surface modeling, the regression method was performed on experimental results to build mathematical models. The models were then formulated as an objective function in an optimization problem that was consequently optimized using a genetic algorithm and sequential quadratic programming approach to obtain the maximum growth rate of the probiotics. The results showed that the quadratic models appeared to have the most accurate response surface fit. Both SQP and GA were able to identify the optimal combination of prebiotics to stimulate the growth of probiotics in milk. Comparing both methods, SQP appeared to be more efficient than GA at such a task.

• 응용통계연구
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• 제29권4호
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• pp.699-706
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• 2016

검정절차가 생략된 이상치 탐지법은 구조적으로 수렁효과나 가면효과에 취약하기 때문에 다수의 이상치를 제대로 탐지하지 못할 때가 있다. 본 연구에서는 군집화에 의하여 구분된 소수 관찰치군을 이상치로 판정하는 방법에 보완될 검정절차를 다룬다. 이에 관련된 일반적인 방법은 탐지된 이상치 후보군의 개별적인 관찰치에 대해 다양한 종류의 t-검정을 수행하는 것이다. 본 연구에서는 이상치 후보군에 대한 검정을 수행하고 군집나무의 절단기준을 변경시켜 새로운 이상치군을 탐색해 나가는 순차적인 방법을 제안한다. 예제와 모의실험을 통해 제시된 방법과 기존의 방법들을 비교한다.

• 국제지역연구
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• 제18권3호
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• pp.47-66
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• 2014

본 연구는 순차적 직접투자가 단일 직접투자에 비해 창출하는 경쟁우위를 이론적으로 규명한 후 이를 실증적으로 검증하고자 했다. 이는 대(對)중국 직접투자를 시행하는 우리나라 기업들이 주로 일회성 투자에 그치지 않고 후속투자를 거쳐 중국 시장 내 생산거점을 확보하려 한다는 전략적 행동에 기반 한 것이다. 이를 이론적으로 접근하기 위해 수량경쟁모형을 구축한 후 명제들을 추출해 냈다. 미래가치를 포함 해 도출된 균형점들은 첫째, 순차적 직접투자는 모기업들의 생산을 증대시키는 효과를 발생시킬 것이고, 둘째, 일회성 투자에 그치는 경우에 비해 순차적 투자는 큰 미래가치를 창출할 수 있으며, 셋째, 대(對)중국 순차적 직접투자는 장기적으로 진행될수록 기업단위에서 보다 큰 생산효과를 거둘 수 있다는 점을 제시한다. 이는 중국시장에서의 노하우가 축적될수록 경쟁우위가 강화될 것임을 의미한다. 이러한 이론적 명제들을 검증하고자 대(對)중국 직접투자를 시행한 우리나라 모기업들을 대상으로 회귀분석을 시도했는데, 그 결과 수량경쟁 모형으로부터 도출된 명제들이 지지되었다. 주요 결과를 정리하면 첫째, 순차적 투자는 모기업의 생산력을 증대시키는 것으로 나타났고, 둘째, 중국 내 활동기간에 비례해 중국 내 총 자회사 수가 증가함을 알 수 있다. 셋째, 중국 투자 이전 해외진출경험은 대(對)중국 순차적 직접투자를 시행한 모기업의 경영성과를 유의적으로 개선시키고, 대(對)중국 순차적 직접투자가 증가할수록 모기업의 생산성이 동반상승한다.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.273-293
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• 2019

We consider the problem of model selection in multiple linear regression with outliers and non-normal error distributions. In this article, the robust model selection criterion is proposed based on the robust estimation method with the least absolute deviation (LAD). The proposed criterion is shown to be consistent. We suggest proposed criterion based algorithms that are suitable for a large number of predictors in the model. These algorithms select only relevant predictor variables with probability one for large sample sizes. An exhaustive simulation study shows that the criterion performs well. However, the proposed criterion is applied to a real data set to examine its applicability. The simulation results show the proficiency of algorithms in the presence of outliers, non-normal distribution, and multicollinearity.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.155-166
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• 2002

Neural networks have been studied as a popular tool for classification and they are very flexible. Also, they are used for many applications of pattern classification and pattern recognition. This paper focuses on Bayesian approach to feed-forward neural networks with single hidden layer of units with logistic activation. In this model, we are interested in deciding the number of nodes of neural network model with p input units, one hidden layer with m hidden nodes and one output unit in Bayesian setup for fixed m. Here, we use the latent variable into the prior of the coefficient regression, and we introduce the 'sequential step' which is based on the idea of the data augmentation by Tanner and Wong(1787). The MCMC method(Gibbs sampler and Metropolish algorithm) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data.