• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.995-1005
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• 2009

Conditional correlation between financial time series plays an important role in risk management, asset allocation and portfolio selection and therefore diverse efforts for modeling conditional correlations in multivariate-GARCH processes have been made in last two decades. In particular, CCC (cf. Bollerslev, 1990) and DCC(dynamic conditional correlation, cf. Engle, 2002) models have been commonly used since they are relatively parsimonious in the number of parameters involved. This article is concerned with DCC modeling for multivariate GARCH processes in comparison with CCC specification. Various multivariate financial time series are analysed to illustrate possible advantages of DCC over CCC modeling.

• Journal of the Korean Magnetic Resonance Society
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• v.16 no.1
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• pp.46-66
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• 2012

Metabolomic studies using human urine have shown that human metabolism is altered by a variety of environmental, cultural, and physiological factors. Comprehensive information about normal human metabolite profiles is necessary for accurate clinical diagnosis of disease and for disease prevention and treatment. In this study, metabolite correlation analyses, using $^1H$ nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistics, were performed on human urine to compare metabolic differences based on gender and/or obesity in healthy human subjects. First, we applied partial least squares discriminant analysis to the NMR spectral data set to verify the data's ability to discriminate by gender and obesity. Then, the differences in metabolite-metabolite correlation between male and female, and between normal and high body mass index (obese) subjects were investigated through pairwise correlations. Creatine and several metabolites, including isoleucine, trans-aconitate, and trimethylamine N-oxide (TMAO), exhibited different quantitative relationships depending on gender. Dimethylamine had a different correlation with glycine and TMAO, based on gender. The correlation of TMAO with amino acids was considerably lower in obese, compared to normal, subjects. We expect that the results will shed light on the metabolic pathways of healthy humans and will assist in the accurate diagnosis of human disease.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.407-410
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• 1996

This paper presents how to build a human sensibility database by multivariate method. And, we discribe a fuzzy based inference system which converts human sensibility data to design factors using the human sensibility database. We are able to obtain the values of multiple correlation coeffcient, partial correlation coefficient, and categories by the quantification theory which is multivariate analysis. So, the human sensibility database is constructed from those values. The inference system will be more useful, if the human sensibility database and graphic design factor database were integrated.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.613-635
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• 2001

This paper proposes a skewed multivariate probit model for analyzing a correlated binary response data with covariates. The proposed model is formulated by introducing an asymmetric link based upon a skewed multivariate normal distribution. The model connected to the asymmetric multivariate link, allows for flexible modeling of the correlation structure among binary responses and straightforward interpretation of the parameters. However, complex likelihood function of the model prevents us from fitting and analyzing the model analytically. Simulation-based Bayesian inference methodologies are provided to overcome the problem. We examine the suggested methods through two data sets in order to demonstrate their performances.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.273-280
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• 2001

In order to evaluate the stability of railway cut-slope under rainfall, explanatory variables and subordinate variables were selected for multivariate analysis. Furthermore the site which had occurred failure due to rainfall was investigated, and by executing multivariate analysis for 121 cases, critical rainfall was defined by the case that had high value of correlation factor. The 0.3 square value of maximum hourly rainfall during 24 hours before failure caused the collapse of railway cut-slope and could be used to estimate the stability of railway cut-slope. From the result of application to a collapse example, the evaluaton method by critical rainfall curve is satisfactory.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.471-478
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• 2012

In this paper, we present a permutation test to select the number of pairs of canonical variates in canonical correlation analysis. The existing chi-squared test is known to be limited to normality in use. We compare the existing test with the proposed permutation test and study their asymptotic behaviors through numerical studies. In addition, we connect canonical correlation analysis to regression and we we show that certain inferences in regression can be done through canonical correlation analysis. A regression analysis of real data through canonical correlation analysis is illustrated.

• Korean Journal of Clinical Oncology
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• v.14 no.2
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• pp.89-94
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• 2018

Purpose: To investigate the relationship between MUC expression and clinicopathologic factors in advanced gastric cancer. Methods: A total of 237 tumor specimens were assessed for MUC expression by immunohistochemistry. The clinicopathologic factors were investigated with MUC1, MUC2, MUC5AC, and MUC6. Results: MUC1, MUC2, MUC5AC, and MUC6 expression was identified in 148 of 237 (62.4%), 141 of 237 (59.5%), 186 of 237 (78.5%), and 146 of 237 (61.6%) specimens, respectively. MUC1 expression was correlated with age, human epidermal growth factor receptor 2 (HER2) status, lymphatic invasion, Lauren classification and histology. Further multivariate logistic regression analysis revealed a significant correlation between MUC1expression and lymphatic invasion, diffuse type of Lauren classification. MUC5AC expression was correlated with HER2 status, Lauren classification and histology. Further multivariate logistic regression analysis revealed a significant correlation between MUC5AC expression and HER2 status, diffuse and mixed type of Lauren classification. MUC2 and MUC6 expression were not correlated with clinicopathologic factors. The patients of MUC1 expression had poorer survival than those without MUC1 expression, but MUC2, MUC5AC or MUC6 were not related to survival. In an additional multivariate analysis that used the Cox proportional hazards model, MUC1 expression was not significantly correlated with patient survival independent of age, N-stage, and venous invasion. Conclusion: When each of these four MUCs expression is evaluated, in light of clinicopathologic factors, MUC1 expression may be considered as a prognostic factor in patients with advanced gastric cancer. Therefore, careful follow-up may be necessary because the prognosis is poor when MUC1 expression is present.

• International Journal of Automotive Technology
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• v.4 no.2
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.