• Communications for Statistical Applications and Methods
• /
• v.16 no.4
• /
• pp.675-686
• /
• 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
• /
• v.15 no.3
• /
• pp.531-542
• /
• 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
• /
• v.16 no.2
• /
• pp.349-357
• /
• 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
• /
• v.5 no.1
• /
• pp.25-33
• /
• 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.

• Korean Journal of Food Science and Technology
• /
• v.23 no.6
• /
• pp.711-716
• /
• 1991

Various multivariate statistics were applied to determine the relationships between sensory properties of 9 pre-cooked foods. Twelve sensory terms were selected to differentiate the food samples in stepwise discriminant analysis. Three factors accounted for 61.9% of total variation of 12 sensory attributes detected. Factor I was highly related to the qualitative sensory terms, while factor II to the quantitative ones. The principal component plot made it possible to define the relationships between sensory properties and food samples. In cluster analysis using average linkage and Ward's method, nine pre-cooked foods were classified into three clusters in terms of their sensorial similarities.

• The Korean Journal of Applied Statistics
• /
• v.21 no.1
• /
• pp.19-33
• /
• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

• Journal of Korean Society on Water Environment
• /
• v.29 no.3
• /
• pp.317-328
• /
• 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.

• Journal of Environmental Science International
• /
• v.16 no.3
• /
• pp.369-376
• /
• 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.

• Journal of Korean Society on Water Environment
• /
• v.23 no.1
• /
• pp.161-168
• /
• 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.

• Journal of Sasang Constitutional Medicine
• /
• v.18 no.3
• /
• pp.131-144
• /
• 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.