• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.675-686
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• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.531-542
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• 2004

Principal component analysis and cluster analysis were conducted to comprehensively evaluate the water quality of Busan coastal area with the data collected seasonally by the analysis of surface water at 10 stations from 1997 to 2003. We noted that the first principal component was regarded as a factor related with the input of nutrient-rich fresh water and the second principal component as meteorological characteristics. Also we obtained that water qualities of station 4 and 9 were different from those of other stations in Busan coastal area.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.349-357
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• 2005

Principal component analysis(PCA) was carried out to evaluate the water quality with the monitering data collected from 1997 to 2003 along the coastal area of Ulsan, Korea. To enhance evaluation and to complement descriptive power of traditional PCA, optimal scaling was applied to transform the original data into optimally scaled data. Cluster analysis was also applied to classify the monitering stations according to their characteristics of water quality.

• Communications for Statistical Applications and Methods
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• 제5권1호
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• pp.25-33
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• 1998

We review a multivariate version of Kendall's tau based on direction vectors of observations. And with this statistic we propose an analog of the forward variable selection method which selects a set of independent variables for further studies to build the eventual predicting model. This method does not assume the distributions of observations and the linear model and it is strong to the outliers with high asymptotic efficiencies relative to the parametric Pearson's correlation coefficient.

• 한국식품과학회지
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• 제23권6호
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• pp.711-716
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• 1991

조리 식품의 관능특성을 평가하고, 다변수 통계법으로 서로의 상관관계를 조사 하였다. 시료 식품을 각각 특징지을 수 있는 12개의 관능특성을 단계적 차별 분석에 의해 선정하였으며, 요민분석에 의해 도출된 3개의 요인으로 12개의 관능 특성이 갖는 변이의 61.9%를 설명할 수 있었다. 요인 I은 질적 관능성질과 관련이 있고, 요인 II는 양적인 관능특성과 높은 상관관계를 나타내었다. 시료 식품과 관능특성을 동시에 주 성분 좌표상에 표시함으로서 서로간의 상관관계 설정이 용이하였고, average linkage 및 Ward's method을 이용한 집락분석에서 9개의 조리식품은 관능특성의 유사성에서 크게 3개의 집락으로 분류되었다.

• 응용통계연구
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• 제21권1호
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• pp.19-33
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• 2008

연속 형질(quantitative trait)에 영향을 미치는 유전자를 알아내기 위해 형제 쌍의 자료를 수집하여, 주로 이용되는 Haseman과 Elston (1972)의 최소제곱 회귀검정법으로 분석하는데 이는 단일 형질에 대한 분석법이다. 현실적으로 여러 형질들이 복잡하게 단일유전자 좌위(single locus)와 연관되어 있어 함께 수집하게 되는 경우에는, 이러한 연관된 여러 형질을 동시에 분석하는 유전연관성 검정법(linkage test)이 절실히 필요한 실정이다. Amos 등 (1990)은 주성분(principal component) 선형모형을 이용하여 Haseman과 Elston (1972)방법을 둘 이상의 형질의 다변량 분석법으로 확장시켰다. 그러나 이 검정방법은 통계량의 분포를 알 수 없기에 아직 제 1종 오류가 제대로 통제되지 못하는 문제를 가지고 있다. 본 논문에서는 이러한 다변량 형질 자료의 연관성검정에 있어 단일변량에 대한 비모수 추세검정법을 다변량 자료에 대한 분석법으로 확장시킨 통계량을 사용할 것을 제안한다. Amos 등 (1990)이 제안한 방법과 다변량 추세검정 통계량을 모의실험으로 생성한 연속형 형질자료에 적용하였을 때, 다변량 추세검정 통계량은 Amos 등 (1990) 방법에서의 여러 문제점이 발생되지 않을 뿐만 아니라 모의실험에서 제 1종 오류가 정해진 유의수준에 가까운 것을 확인하였고, 검정적이 더 높음을 볼 수 있었다.

• 한국물환경학회지
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• 제29권3호
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• pp.317-328
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• 2013

The purpose of this study was to investigate the change of phytoplankton fluctuation and long term of water quality of Lake Juam and to evaluate the relationship between phytoplankton pattern and environmental factors data. Correlation and factor analyses were employed to identify key environmental factors affecting phytoplankton dynamics. Of 18 parameters, pH, temperature, COD, BOD and T-P were highly correlated with Chl-a. Phytoplankton data showed that cyanobacteria were dominant, and more than 60% of total algae density. Also Lake Juam received a lot of influence of the Asian monsoon climate. This study presents necessity of multivariate statistic techniques for evaluation of Lake Juam complex data set with a view to get better information data and effective management of water source.

• 한국환경과학회지
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• 제16권3호
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• pp.369-376
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• 2007

This study was conducted to evaluate water quality in coastal sea of Kunsan using multivariate analysis. The analysis data in Coastal Sea of Kunsan use of surveyed data by the NFRDI from April 2000 to November 2002. Twelve water Quality parameter were determined on each sample. The results was summarized as follow ; Water quality in coastal sea of Kunsan could be explained up to 62.782% by four factors which were included in loading of nitrogen-nutrients by Keum river(24.688%), suspended solids variation (12.180%), seasonal climate variation (18.367%) and variation of DIP (10.546%). To analyze spatially and monthly variation by factor score, it was divided by inner area and outer area spatially, and spring and summer monthly. The result of time series analysis by factor score, inner area of Kunsan coastal sea(St.1 and St. 2) was the most affected by nitrogen-nutrient and suspended solids due to runoff by Keum river. It could be suggested from these results that it is important to reduce tile pollution loads from Kuem river for the control of the water quality in coastal sea of Kunsan.

• 한국물환경학회지
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• 제23권1호
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• 사상체질의학회지
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• 제18권3호
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• pp.131-144
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• 2006

1. Objectives This research uses the DNA chip, which includes 16,383 gene code, and various statistic prediction way that shows objectification index for the objectification of constitution diagnosis. 2. Methods Drawing blood whose constitution is confirmed, and analyze its gene information by using 1.7k DNA chip to find the gene correlation through multivariate statistical method. 3. Results and Conclusions Distinctive genes such as AK001919, U09384, NM_001805, X99962, NM_004796, AK026738, AL050148, BC002538, AK027074, AK026219, AF087962, AL390142, NM_015372, AL157466, NM_002446, AK024523, NM_014706, NM_014746 and AL137544 were related to Taeumin; AL157448, NM_005957, NM_005656, NM_017548, AK027246, NM_003025, NM_012302 and NM_005905 were represented in Soeumin, while AK026503, AF147325, NM_002076, AF147307, AK001375, NM_003740, NM_005114, AB007890, NM_005505, NM_015900, NM_014936, Z70694, AB023154, U52076, NM_004360, NM_005835, NM_017528, AF087987, NM_014897, AK021720, NM_006420, AJ277915, AK002118 and AK021918 were for Soyangin. This study figured out the possibility to develop the prediction system by sorting each constitution's gene, and research each constitution's distinctive character of manifestation pattern.