• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.607-617
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• 2012

The goodness-of-fit test for multivariate normal distribution is important because most multivariate statistical methods are based on the assumption of multivariate normality. We propose goodness-of-fit test statistics for multivariate normality based on the modified squared distance. The empirical percentage points of the null distribution of the proposed statistics are presented via numerical simulations. We compare performance of several test statistics through a Monte Carlo simulation.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1139-1149
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• 2014

Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.433-443
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• 2003

In this study, we provide a comparison of multivariate Q-techniques in the up-to-date versions of SAS, SPSS, Minitab and S-plus well known to those who study statistics. We can analyze data through the direct Input method(command) in SAS and use of menu method in SPSS, Minitab and S-plus. The analysis performance method is chosen by the high frequency of use. Widely we compare with each Q-techniques form according to input data, input option, statistical chart and statistical output.

• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.71-79
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• 1990

In the linear model which consider both the multivariate parallel-line bioassay and the multivariate linear calibration, this paper presents a Bayesian procedure which is an extension of Hunter and Lamboy (1981) and has several advantages compared with the non Bayesian techniques. Based on the methods of this article we discuss the effect of multivariate calibration and give a numerical example.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.679-688
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• 2008

In multivariate regression analysis there exist many influence measures on the regression estimates. However it seems to be few of influence diagnostics on test statistics in hypothesis testing. Case-deletion approach is fundamental for investigating influence of observations on estimates or statistics. Tang and Fung (1997) derived single case-deletion of the Wilks' ratio, Lawley-Hotelling trace, Pillai's trace for testing a general linear hypothesis of the regression coefficients in multivariate regression. In this paper we derived more extended form of those measures to deal with joint influence among observations. A numerical example is given to illustrate the effect of joint influence on the test statistics.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.605-612
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• 2012

Multivariate data are often generated with multiple outcomes in various fields. Multiple outcomes could be mixed as continuous and discrete. Because of their complexity, the data are often dealt with by separately applying regression analysis to each outcome even though they are associated the each other. This univariate approach results in the low efficiency of estimates for parameters. We study the efficiency gains of the multivariate approaches relative to the univariate approach with the mixed data that include continuous and binary outcomes. All approaches yield consistent estimates for parameters with complete data. By jointly estimating parameters using multivariate methods, it is generally possible to obtain more accurate estimates for parameters than by a univariate approach. The association between continuous and binary outcomes creates a gap in efficiency between multivariate and univariate approaches. We provide a guidance to analyze the mixed data.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.11-19
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• 1996

Canonical correlation analysis is a multivariate technique for identifying and quantifying the statistical relationship between two sets of variables. Like most multivariate techniques, the main objective of canonical correlation analysis is to reduce the dimensionality of the dataset. It would be particularly useful if high dimensional data can be represented in a low dimensional space. In this study, we will construct statistical graphs for paired sets of multivariate data. Specifically, plots of the observations as well as the variables are proposed. We discuss the geometric interpretation and goodness-of-fit of the proposed plots. We also provide a numerical example.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.319-332
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• 1997

The application of multivariate linear rank statistics to data with item nonresponse is considered. Only a modest extension of the complete data techniques is required when the missing data may be thought of as a random sample, and an appropriate modification of the covariances is derived. A proof of the asymptotic multivariate normality is given. A review of some related results in the literature is presented and applications including longitudinal and repeated measures designs are discussed.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.747-757
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• 2017

We investigate multivariate volatilities based on high frequency time series. The PCA (principal component analysis) method is employed to achieve a dimension reduction in multivariate volatility. Multivariate realized volatilities (RV) with various frequencies are calculated from high frequency data and "optimum" frequency is suggested using PCA. Specifically, RVs with various frequencies are compared with existing daily volatilities such as Cholesky, EWMA and BEKK after dimension reduction via PCA. An analysis of high frequency stock prices of KOSPI, Samsung Electronics and Hyundai motor company is illustrated.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.858-865
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• 2014

In the case of system like MES, various sensors collect the data in real time and save it as a big data to monitor the process. However, if there is big data mining in distributed computing system, whole processing process can be improved. In this paper, system to analyze the cause of operation deviation was built using the big data which has been collected from deasphalting process at the two different plants. By applying multivariate statistical analysis to the big data which has been collected through MES(Manufacturing Execution System), main cause of operation deviation was analyzed. We present the example of analyzing the operation deviation of deasphalting process using the big data which collected from MES by using multivariate statistics analysis method. As a result of regression analysis of the forward stepwise method, regression equation has been found which can explain 52% increase of performance compare to existing model. Through this suggested method, the existing petrochemical process can be replaced which is manual analysis method and has the risk of being subjective according to the tester. The new method can provide the objective analysis method based on numbers and statistic.