• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.97-107
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• 1999

프로파일 분석은 반복측정 자료를 분석하는데 있어서 널리 사용되는 다변량 분석모형이다. 프로파일 분석에서는 처리 그룹간의 비교와 반응 프로파일의 평행성 검정을 위해서 4가지 검정통계량이 널리 사용되고 있다. 이들 검정통계량은 Wilks의 통계량($\Lambda$), Pillai's Trace 통계량(V), Hotelling-Lawley Trace 통계량(U), Roy's Maximum Root 통계량($\Theta$ )이다. 그 동안 이들 통계량들을 비교하기 위한 여러 연구가 있었지만 주로 일반적인 다변량 분산분석 모형에 근거한 비교였다. 본 논문에서는 자료가 반복측정 자료이고 우리의 관심이 프로파일 분석에 있을 때에 이 4가지 통계량의 비교에 초점을 맞추었다.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.243-248
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• 2002

최근 다변량자료분석과 관련하여 이를 시스템으로 구현하려는 연구가 다양한 각도로 이루어지고 있다. 이러한 연구들의 공통적인 특징은 일반 사용자들에게 고급 통계분석기법을 편리하게 활용할 수 있도록 GUI(Graphical User Interface) 환경의 시스템을 제공해 준 것이다. 이런 연구의 연장선상에서 본 연구에서는 다변량자료분석 시스템을 구현하는데 있어 사회 각 분야에서 가장 널리 활용되고 있는 사무용 프로그램인 마이크로소프트(Microsoft) Excel을 활용하여 일반 사용자들도 다변량분석을 쉽게 활용할 수 있도록 대화식 시스템을 개발하였다.

• 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.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.257-267
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• 2020

We introduce multivariate exponential smoothing models based on a vector innovations structural time series framework. The models enable us to exploit potential inter-series dependencies to improve the fit and forecasts of multivariate (vector) time series. Models are applied to forecast the exchange rates of the UK pound (UKP) and US dollar (USD) against the Korean won (KRW) observed on monthly basis; subseqently, we compare their performance with alternative models. We observe that the multivariate exponential smoothing models are superior to alternatives.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.579-591
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• 2022

Functional data is collected in various fields. It is often necessary to test whether there are differences among groups of functional data. In this case, it is not appropriate to explain using the point-wise ANOVA method, and we should present not the point-wise result but the integrated result. Various studies on functional data analysis of variance have been proposed, and recently implemented those methods in the package fdANOVA of R. In this paper, I first explain ANOVA and multivariate ANOVA, then I will introduce various methods of analysis of variance for univariate and multivariate functional data recently proposed. I also describe how to use the R package fdANOVA. This package is used to test equality of weekly temperatures in Seoul and Busan through univariate functional data ANOVA, and to test equality of multivariate functional data corresponding to handwritten images using multivariate function data ANOVA.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.169-180
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• 2017

Multivariate GARCH models are interested in conditional variances (volatilities) as well as conditional correlations between return time series. This paper is concerned with high-frequency multivariate financial time series from which realized volatilities and realized conditional correlations of intra-day returns are calculated. Existing multivariate GARCH models are reviewed comparatively with the realized volatility via canonical correlations and value at risk (VaR). Korean stock prices are analysed for illustration.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.223-231
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• 2001

다변량분석 기법을 위해서는 자료가 정규성(normality)가정을 만족해야한다. 본 연구에서는 GUI환경에서 일변량 및 다변량자료의 정규성검정, 이상치제거 및 변수변환을 하는 시스템을 Visual Basic 언어로서 구축하여 사용자들이 보다 편리하게 사용할 수 있음을 소개 하고자 한다.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.253-255
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• 2000

다변량분석 기법을 사용하기 위해서는 자료가 정규성(normality)가정을 만족해야한다. 본 연구에서는 GUI(graphic user interface)환경 하에서 일변량(univariate)과 다변량자료(multivariate data)의 정규성검정, 이상치(outliers)제거 및 변수변환(variable transformation)을 지원하는 시스템을 구축하여 사용자들이 보다 편리하게 사용할 수 있음을 소개 하고자 한다.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.153-164
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• 2004

In this study, we compare multivariate R-techniques in the up-to-date versions of SAS, SPSS, Minitab and S-plus. The direct input method by typing in command is considered for SAS, while the menu-driven method is considered for SPSS, Minitab and S-plus. Comparison was made in terms of input data format, input option, charts and outputs.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.567-578
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• 2017

Grouped multivariate data can be tested for differences between two or more groups using multivariate analysis of variance (MANOVA). However, this method cannot be used if several assumptions of MANOVA are violated. In this case, multidimensional scaling (MDS) and analysis of distance (AOD) can be applied to grouped dissimilarities based on the various distances. A permutation test is a non-parametric method that can also be used to test differences between groups. MDS is used to calculate the coordinates of observations from dissimilarities and AOD is useful for finding group structure using the coordinates. In particular, AOD is mathematically associated with MANOVA if using the Euclidean distance when computing dissimilarities. In this paper, we study the between and within group structure by applying MDS and AOD to the grouped dissimilarities. In addition, we propose a new test statistic using the group structure for the permutation test. Finally, we investigate the relationship between AOD and MANOVA from dissimilarities based on the Euclidean distance.