• 한국물환경학회지
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• 제37권6호
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• pp.520-530
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• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제10권3호
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• pp.229-238
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• 2006

During World War II there was a statistical analysis conducted by the Allied analysts to estimate the German war productions, including their tank productions. This article revisits the analysis of the tank productions as a classroom activity format. Various reformed ideas are proposed in order to enhance students' perspectives of the point estimation. Comprehensive simulation works and actual classroom discussions will be provided along with the theoretical investigations.

• Journal of the Korean Data and Information Science Society
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• 제16권1호
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• pp.19-31
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• 2005

Brain injured patients who had the driver's license before the injury of the brain were tested with the newly developed tool CPAD by Hangyang Medical School and the National Rehabilitation Center. The CPAD contains many variables to measure the ability of driving. Also for each patient the American standard CBDI score was measured and the result was compared with the CPAD results. Of interest is to classify the patients as pass, border, fail group after the CPAD test. To derive the discriminant functions with the group information based on CBDI, parametric/nonparametric and multivariate/univariate discriminant analysis was performed and discussed.

• Environmental Engineering Research
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• 제24권2호
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• pp.279-288
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• 2019

To obtain insightful knowledge of geochemical process controlling fluoride enrichment in groundwater of the villages near Shilabati river bank, West Bengal, India, multivariate statistical techniques were applied to a subgroup of the dataset generated from major ion analysis of groundwater samples. Water quality analysis of major ion chemistry revealed elevated levels of fluoride concentration in groundwater. Factor analysis (FA) of fifteen hydrochemical parameters demonstrated that fluoride occurrence was due to the weathering and dissolution of fluoride-bearing minerals in the aquifer. A strong positive loading (> 0.75) of fluoride with pH and bicarbonate for FA indicates an alkaline dominated environment responsible for leaching of fluoride from the source material. Mineralogical analysis of soli sediment exhibits the presence of fluoride-bearing minerals in underground geology. Hierarchical cluster analysis (HCA) was carried out to isolate the sampling sites according to groundwater quality. With HCA the sampling sites were isolated into three clusters. The occurrence of abundant fluoride in the higher elevated area of the observed three different clusters revealed that there was more contact opportunity of recharging water with the minerals present in the aquifer during infiltration through the vadose zone.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.271-281
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• 2013

This paper considers a problem of classification of variable stars based on functional data analysis. For a better understanding of galaxy structure and stellar evolution, various approaches for classification of variable stars have been studied. Several features that explain the characteristics of variable stars (such as color index, amplitude, period, and Fourier coefficients) were usually used to classify variable stars. Excluding other factors but focusing only on the curve shapes of variable stars, Deb and Singh (2009) proposed a classification procedure using multivariate principal component analysis. However, this approach is limited to accommodate some features of the light curve data that are unequally spaced in the phase domain and have some functional properties. In this paper, we propose a light curve estimation method that is suitable for functional data analysis, and provide a classification procedure for variable stars that combined the features of a light curve with existing functional data analysis methods. To evaluate its practical applicability, we apply the proposed classification procedure to the data sets of variable stars from the project STellar Astrophysics and Research on Exoplanets (STARE).

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.421-421
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• 2017

현재 우리나라에서 끊임없이 발생하고 있는 폭풍해일로부터 연안지역의 안전을 확보하기 위해서는 태풍 시 파랑의 거동 및 특성을 정확히 예측하는 것이 중요하다. 폭풍해일 모의실험의 정확성을 향상시키고 폭풍해일의 위험성을 정량화하기 위해서는 해일파고, 파주기, 그리고 폭풍 지속시간 간의 상관성이 분석되어야한다. 이를 위해 본 연구에서는 Copulas(Archimedean) 이론을 이용하여 폭풍해일에 대한 다변량 통계분석이 이루어졌다. 동해안 연안에서 나타나는 파고, 파주기, 태풍 지속시간, 해면수위, 태풍 도착간격시간 간의 의존성을 켄달의 타우 상관계수를 이용하여 조사하였다. Copulas 다변량 통계분석의 결과, 오직 파고와 파주기, 그리고 태풍지속시간만이 명확한 상관성을 나타냈다.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.59-79
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• 2021

Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

• 응용통계연구
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• 제20권2호
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• pp.387-394
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• 2007

본 연구는 여러 가지의 양적변수들을 조사하는 다목적, 다변량조사 표본설계에서 층화 문제를 다룬다. 다변량 층화변수를 사용하는 층화 방법으로 일변량 층화변수가 있을 때 사용하는 누적도수제곱근법을 독립적으로 여러 층화변수에 적용하는 방법, 군집분석을 이용하는 방법, 인자분석과 군집분석을 함께 이용하는 방법 등 세 가지 방법을 제시한다. 한편, 2001년 농업총조사 자료에 나타난 동 읍 면의 농기계별 보유대수 정보를 층화변수로 활용하여 세 가지 층화 방안의 효율을 실증적으로 비교하게 되는데 그 결과 인자분석과 군집분석을 함께 고려한 층화방법이 비교적 효율적인 것으로 나타났다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1998년도 봄 학술발표회논문집(II)
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• pp.719-724
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• 1998

It is proposed in this paper to develop the rational mix design system of High-strength concrete which is adjusted in the domestic circumstances. 1) Collect a lots of data in order to introduce the optimum mix design which has relation among material variables which compose High-strength concrete and run by using SAS (Statistical analysis system) which is one of multivariate statistical analysis method. 2) Select the important material variables for mix design of High-strength concrete by major component analysis and propose the standard range of each material variable along the target strengths. From the results of this study, it was proposed the range of proper material variables in domestic circumstance, which are W/C, S/A, air and admixture amounts, etc, at the target strengths for concrete kind. Also it was developed the optimum mix design program of High-strength concrete according to target strength and size of aggregate and made mix design ease in domestic construction site.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.149-161
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• 2020

In this paper we extend the functional principal component analysis for real-valued random functions to the case of Hilbert-space-valued functional random objects. For this, we introduce an autocovariance operator acting on the space of real-valued functions. We establish an eigendecomposition of the autocovariance operator and a Karuhnen-Loève expansion. We propose the estimators of the eigenfunctions and the functional principal component scores, and investigate the rates of convergence of the estimators to their targets. We detail the implementation of the methodology for the cases of compositional vectors and density functions, and illustrate the method by analyzing time-varying population composition data. We also discuss an extension of the methodology to multivariate cases and develop the corresponding theory.