• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.159-168
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• 2014

The news impact curve(NIC) originally proposed by Engle and Ng (1993) is a graphical representation of volatility for financial time series. The NIC is a simple but a powerful tool for identifying variability of a given time series. It is noted that the NIC is suited to symmetric volatility. Recently a lot of attention has been paid to asymmetric volatility models and therefore asymmetric version of the NIC would be useful in the field of financial time series. In this article, we propose to incorporate LASSO in constructing asymmetric NICs based on asymmetric GARCH models. In particular, bilinear GARCH models are considered and illustrated via KOSDAQ data.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.385-392
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• 2015

Principal component analysis (PCA), a dimension-reduction technique, is usually implemented after the variables are standardized when the measurement unit of variables are different. To standardize a variable we divide it by its standard deviation. But there is another way to transform a variable to be independent of its measurement unit. It is to divide it by its mean rather than standard deviation. Implementing PCA on standardized variables is equivalent to implementing PCA with a correlation matrix of original variables. Similarly, implementing PCA on the transformed variables divided by their means is equivalent to implementing PCA with a matrix related to the coefficients of variation of the original variables. We explain why we need to implement PCA on the variables transformed by their means.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.345-355
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• 2007

PLS methods are suited for regressing q-variate Y variables on p-variate X variables even in the presence of multicollinearity problem among X variables. Consequently, they are useful for analyzing datasets with smaller number of observations compared to the number of variables, such as NIR(near-infrared) spectroscopy data in chemometrics. In this study, we propose two visualizing methods of p-variate X variables and q-variate Y variable that can be used in connection with PLS analysis.

• The Korean Journal of Applied Statistics
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• v.21 no.5
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• pp.845-854
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• 2008

Survey reports of election opinions consist of numerous cross-tabulations between socio-demographic variables and political opinions including preferred candidates. Since socio-demographic variables are related each other, duplicate interpretations arise. The aim of this study is twofold: The first is to separate the effects of socio variables such as education, occupation and income from the effects of demographic variables such as region, sex and age. The second is the visualization of multiple cross-tabulations in low-dimensional space by extended doubling technique of correspondence analysis. Survey researchers may get some help from this study to present their survey results more lucidly and visually.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.593-597
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• 2008

유역에서의 강우사상에 따른 일련의 수문학적 과정의 규명과 수자원의 효율적 관리를 위한 토양함수량을 산정하는데 토양수분의 시공간적 분포특성을 파악하는 것은 매우 중요하다. 연구유역은 경기도 파주시 적성면 설마리의 설마천 유역 내에 위치한 소유역이다. 대상유역의 정밀측량을 하여 수치고도모형(DEM)을 획득 하였다. 이 수치고도모형에 사용하여 수치지형분석을 통해 총 21지점을 선정하였다. 토양수분의 연직방향 변화를 알아보기 위해 각 지점의 10, 30, 60cm 깊이에 센서를 설치하여 토양수분을 측정하는 TDR (Time Domain Reflectometry)방식인 MiniTRASE를 이용하여 총 50채널을 통해 매 2시간 간격으로 토양수분의 변동을 관측하였다. 토양수분의 시공간적 분포특성을 분석하기 위해 획득된 자료를 바탕으로 시계열의 공간 분석 및 통계분석을 수행하였다. 토양수분 시계열에 대한 공간분석은 토양수분의 사면에서의 공간적인 분포가 사면의 지형적인 형상에 의해서 영향을 받는다는 것을 보여주고 있다. 그리고 통계분석을 통해 평균치의 표준편차가 대상 기간 동안 일정한 것으로 나타났고, 이는 대상사면에서의 토양수분 분포 특성이 기후나 식생의 변동성에 영향을 받지 않고, 지형이나 토질 같은 정적인 인자에 주로 영향을 받는다는 가설을 뒷받침한다. 이 결과는 토양수분의 시공간적 분포양상의 파악과 국내 사면에서의 수문기작들을 규명하는데 기여를 할 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.43 no.11
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• pp.933-944
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• 2010

This study compares the CEM and SGS methods which are geostatistical stochastic simulation methods for assessing the uncertainty by spatial variability in the estimation of the spatial distribution of probability rainfall. In the stochastic simulations using CEM and SGS, two methods show almost similar results for the reproduction of spatial correlation structure, the statistics (standard deviation, coefficient of variation, interquartile range, and range) of realizations as uncertainty measures, and the uncertainty distribution of basin mean rainfall. However, the CEM is superior to SGS in aspect of simulation efficiency.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.172-172
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• 2021

주된 물관리 시설의 신뢰성 있는 운영 계획의 수립을 위하여 충분한 길이의 유입량을 확보하는 것은 중요하나 현실적으로 제한된 관측 자료만 존재한다. 본 연구에서는 충분한 길이의 유입량을 생성하기 위하여 유입량의 모의 방법론을 제안하고자 한다. 제안하는 모형은 크게 3가지의 방법론을 기반으로 한다. 첫 번째는 연 유입량과 월 유입량의 생성단계로 Wavelet 기반으로 Autoregressive-moving-average(ARMA)을 적용할 것이다. 다음으로 일 유입량의 생성에 있어서 과거 관측값을 기반으로 한 Z-Score-based jittering 방법론을 적용할 것이다. 이렇게 각각 생성된 연 유입량, 월 유입량 그리고 일 유입량을 K-Nearest Nedighbors (K-NN) 방법론을 이용하여 최종 유입량을 결정하고자 한다. 생성된 유입량의 유용성을 판단하기 위하여 본 연구에서는 단기와 장기에서의 시계열의 지속성을 허스트 지수와 상관계수를 사용하여 검증할 것이며 이를 과거 관측치와 비교하고자 한다. 또한 각각의 연, 월, 일별의 기준으로 주요 통계치인 평균과 표준편차를 과거 관측 시계열의 통계치와 비교하여 그 유용성을 판단할 것이다.

• Journal of agricultural medicine and community health
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• v.31 no.2
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• pp.177-185
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• 2006

Objectives: This study was carried out with 31, 519(16,653 boys, 14,857 girls) of elementary school students to investigate the prevalence of obesity at a district in Gwangju City. It can be applied to develope an educational program of the obesity control as basic data in this local area Methods: The data collected from May, 2004 to July, 2004 were analyzed by SAS PC+ 8.0 program. Children were selected depending on criteria from obesity index (%) by using physical index (height, body weight), and then subjects were classified into one of three groups according to the degree of obesity: mild(20~29.9%), moderate(30~49.9%), and severe($?50%{\cdot}$) obesity. Results: It showed that male elementary school students were higher and heavier than female elementary school students(p< .001) in every grade except the 4th grade(height) and the 6th grade ( body weight). The obesity rates of male students(11.6%) showed higher(p< .001) than those of female students(8.8%). Specially the 4th grade elementary school boys were higher than any other groups in obesity(13.7%). As a whole, the prevalence of obestiy showed mild(5.9%), moderate(3.8%), and severe(0.6%). Male students showed higher rate of obesity than those of female students. The obesity of male students showed higher rate than that of female students except 2-3rd grade elementary school students(p< .001). The obesity of 4~6th grade elementary school students showed higher rate than those of 1~3rd grade students(p< .001). Conclusions: The obesity rates of male students are higher than that of female students, and the obesity rates of 4~6th grade students are higher than those of 1-3rd grade students in the elementary school. Additionally, these results suggest that the program may be needed to prevent obesity of children.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.756-763
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• 2013

Bilateral filter (BF), functioning by two Gaussian filters, domain and range filter is a nonlinear filter for sharpness enhancement and noise removal. In infrared (IR) small target detection field, the BF is designed by background predictor for predicting background not including small target. For this, the standard deviations of the two Gaussian filters need to be changed adaptively in background and target region of an infrared image. In this paper, the proposed bilateral filter make the standard deviations changed adaptively, using variance feature of mean values of surrounding block neighboring local filter window. And, in case the variance of mean values for surrounding blocks is low for any processed pixel, the pixel is classified to flat background and target region for enhancing background prediction. On the other hand, any pixel with high variance for surrounding blocks is classified to edge region. Small target can be detected by subtracting predicted background from original image. In experimental results, we confirmed that the proposed bilateral filter has superior target detection rate, compared with existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.956-962
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• 2018

As the study of the object feature detection from image, we explain methods to identify the species of the tree in forest using the picture taken from dron. Generally there are three kinds of methods, which are GLCM (Gray Level Co-occurrence Matrix) and Gabor filters, in order to extract the object features. We proposed the object extraction method using the statistical properties of trees in this research because of the similarity of the leaves. After we extract the sample images from the original images, we detect the objects using cross correlation techniques between the original image and sample images. Through this experiment, we realized the mean value and standard deviation of the sample images is very important factor to identify the object. The analysis of the color component of the RGB model and HSV model is also used to identify the object.