• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.35-43
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• 2011

In this paper, we introduce a unit root test via discrete cosine transform in the AR(1) process. We first investigate the statistical properties of DCT coefficients under the stationary AR(1) process and the random walk process in order to verify the validity of the proposed method. A bootstrapping approach is proposed to induce the distribution of the test statistic under the unit root. We performed simulation studies for comparing the powers of the Dickey-Fuller test and the proposed test.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1041-1051
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• 2011

The development of information technology provides us with more useful information but it arose to protect such information from inappropriate users. In the course of analyzing and managing the risks associated with information, it should be needed to accurately measure the value of information. We try to consider the contingent valuation method for this purpose. The contingent valuation method which is used to assess the value of public goods or nonmarket goods makes an statistical estimation for the willingness-to-pay. We show with an example how we can estimate the value of information by calculating the amount we are willing to pay the value of information that exists on the information system. Calculation is carried out by using R.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.697-708
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• 2008

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes, people and systems, or external events. The advanced measurement approach proposed by Basel committee uses loss distribution approach(LDA) which quantifies operational loss based on bank's own historical data and measurement system. LDA involves two distribution fittings(frequency and severity) and then generates aggregate loss distribution by employing mathematical convolution. An objective validation for the operational risk measurement is essential because the operational risk measurement allows flexibility and subjective judgement to calculate regulatory capital. However, the methodology to verify the soundness of the operational risk measurement was not fully developed because the internal operational loss data had been extremely sparse and the modeling of extreme tail was very difficult. In this paper, we propose a methodology for the validation of operational risk measurement based on bootstrap confidence intervals of operational VaR(value at risk). We derived two methods to generate confidence intervals of operational VaR.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.39-52
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• 2002

Traffic data by vehicle classification is difficult for mutual exchange of data due to the different vehicle classification from each other by the data sources; as a result, application of the data is very limited. In Particular. in case of TCS vehicle classification in national highways, passenger car, van and truck are mixed in one category and the practical usage is very low. The research standardize the vehicle classification to convert other data and develop the model which can estimate national highway traffic data by the standardized vehicle classification from the raw traffic data obtained at the highway tollgates. The tollgates are categorized into several groups by their features and the model estimates traffic data by the standardized vehicle classification by using the point estimation and bootstrap algorithm. The result indicates that both of the two methods above have the significant level. When considering the bias of the extreme value by the sample size, the bootstrap algorithm is more sophisticated. Using result of this study, we is expect the usage improvement of TCS data and more specific comparison between the freeway traffic investigation and link volume on freeway using the TCS data.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1845-1849
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• 2006

본 연구에서는 관측자료에 나타난 Clark 단위도의 매개변수를 검토하고 그 변동성을 평가하였다. 강우-유출과정에 영향을 미치는 유역 및 기상 특성인자들을 확률밀도함수로 정량화하였고, 유역의 집중시간 및 저류상수를 호우사상의 특성 및 유역의 조건을 고려하여 다변량 회귀분석을 실시하였다. 이를 Monte Carlo 모의기법에 적용하여 유역평균 저류상수 및 집중시간에 대한 신뢰구간을 추정하였다. 또한 신뢰구간을 좁히기 위한 방안으로 관측된 집중시간 및 저류상수를 Bootstrap 기법으로 처리하였다. 그 결과 유역을 대표하는 유출특성의 결정에는 관측 강우-유출사상의 수가 어느 정도 확보된다고 하더라도 여전히 높은 불확실성을 피하기 힘들다는 것이다. 집중시간의 경우는 그 분포가 상당히 왜곡된 형태여서 단순한 산술평균은 상당히 왜곡된 추정치를 제시할 가능성이 높다. 단순히 관측치를 이용한 경우보다 Monte Carlo 모의기법에 의한 경우 신뢰구간이 2-3배정도 좁게 나타났다. 어느 정도 신뢰도 있는 집중시간 및 저류상수의 추정을 위해서는 최소 10여개 대략 20개 정도 이상의 호우사상이 필요할 것으로 판단된다. 본 연구의 목적은 주어진 유역을 대표할 수 있는 집중시간 및 저류상수를 결정하고자 하는 것이다. 따라서 본 연구에서의 방법론을 적용하여 관측자료가 존재하는 다양한 유역에서의 집중시간 및 저류상수를 결정하고, 이를 지형인자 유역특성을 고려하여 회귀분석하는 경우 보다 정도 있는 경험식의 개발도 가능할 것이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.12-17
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• 2003

Optimal determination of cluster size has an effect on the result of clustering. In K-means algorithm, the difference of clustering performance is large by initial K. But the initial cluster size is determined by prior knowledge or subjectivity in most clustering process. This subjective determination may not be optimal. In this Paper, the genetic algorithm based optimal determination approach of cluster size is proposed for automatic determination of cluster size and performance upgrading of its result. The initial population based on attribution is generated for searching optimal cluster size. The fitness value is defined the inverse of dissimilarity summation. So this is converged to upgraded total performance. The mutation operation is used for local minima problem. Finally, the re-sampling of bootstrapping is used for computational time cost.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.

• Journal of Life Science
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• v.13 no.6
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• pp.856-864
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• 2003

The 16S rRNA gene is the most common gene in the phylogenetic analysis of procaryotes. However very high conservative of 16S rRNA has limitation in the discrimination of highly related organisms, hence other molecule was applied in this study and the result was compared with that of 16S rRNA. Three COGs (Clusters of Orthologous of protein) were only detected in 42 procaryotes ; transcription elongation facto. (COG0195), bacterial DNA primase (COG0358) and uridylate kinase (COG0528). Uridylate kinase gene was selected because of the similarity and one single copy number in each genome. Bacteria, belong to same genus, and Archaebacteria were same position with high bootstrap value in phylogenetic tree like the tree of 16S rRNA. However, alpha and epsilon Proteobcteria showed different position and Spirochaetales of Eubarteria was grouped together with Archaebacteria unlike the result of 16S rRNA. Uridylate kinase may compensate the problem of very high conservative of 16S rRNA gene and it would help to access more accurate discrimination and phylogenetic analysis of bacteria.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.537-546
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• 2008

One of main goals of time series analysis is to estimate prediction of future values. In this paper, we investigate the bias problem when the transformation and back- transformation approach is applied in ARMA models and introduce a modified smearing estimation to reduce the bias. An empirical study on the returns of KOSDAQ index via Yeo-Johnson transformation was executed to compare the performance of existing methods and proposed methods and showed that proposed approaches provide a bias-reduced estimation of the prediction value.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.184-188
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• 2008

추계학적 시계열 분석은 크게 수문자료의 장기간 합성과 실시간 예측으로 구분해 볼 수 있다. 장기간 합성은 주로 수문자료의 추계적 특성을 반영한 수자원 시스템의 운영율 개발에 이용되어 왔다. 반면에 실시간 예측은 수자원 시스템의 순응적(adaptive) 관리에 적용되고 있다. 두 개념의 차이로 전자는 시계열 자료를 합성하여 발생 가능한 모든 수문조합을 얻고자 하는 것이라면 후자는 전 시간의 수문량을 조건으로 하는 다음 시간의 값을 순응적으로 예측하는 것이라 할 수 있다. 수문자료의 합성과 예측에는 크게 결정론적, 확률론적 방법의 두 가지 대별될 수 있다. 결정론적 모델링 방법에는 인공신경망이나 Fuzzy 기법 등을 이용할 수 있으며, 확률론적 방법에는 ARMAX 등의 모수적 기법과 k-NN(k-nearest neighbor bootstrap resampling), KDE(kernel density estimates), 추계학적 인공신경망 등의 비모수적 기법으로 분류할 수 있다. 본 연구에서는 대표적 비모수적 기법인 k-NN를 이용하여 충주댐을 대상으로 월 및 일 유입량 자료의 예측 정도를 살펴보았다. 전 시간 관측치를 조건으로 하는 다음 시간의 조건부 확률분포를 구하여 평균값을 계산한 후 관측치와 비교함으로써 모형의 정도를 살펴보았다. 그리고 실시간 저수지 운영에 이 기법의 활용성과 장단점도 살펴보았다. 모형개발 절차로 모형의 보정을 거쳐 검증을 실시하였다. 결론적으로 월 및 일 유입량 예측에 k-NN 기법이 실무적으로 적용될 수 있었으며, 장점으로는 k-NN 기법이 다른 기법보다 모델링 절차가 비교적 쉬워 저수지 운영 최적화 등 타 시스템과의 연계에 수월함이 인식되었다.