• Journal of Korean Society of Environmental Engineers
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• v.37 no.3
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• pp.137-144
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• 2015

The purpose of this study is to develop an warning system to detect real-time water quality abnormality using a multivariate statistical approach. In this study, we applied principal component analysis among multivariate data analyses which was used for the correlation between water quality parameters considering the real-time algorithm to determine abnormality in water quality. We applied our approach to real field data and showed the utilization of algorithm for the real-time monitoring to find water quality abnormality. In addition, our approach with Korea Meterological Adminstration database identified heavy rain data due to climate change is one of the most important factors to explain water quality abnormality.

• The Korean Journal of Applied Statistics
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• v.1 no.1
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• pp.39-44
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• 1987

For obtaining the m.l.e. of $\Sigma$ from p-variate Non-singular Normal parent, $N_p(\mu, \Sigma)$, Andersous' Procedure based on the invariance property of the m.l.e. seems to be generally Preferred in the view of its simplicity. This paper shows that his approach with respect to $\Sigma^{-1}$ rather than $\Sigma$ itself, he burther applicable to deriving the m.l.e. of parametersinvolved in the common factor model an dsimplex model as well.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.192-197
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• 2020

Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.659-669
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• 2021

Multivariate regression often appears in longitudinal or functional data analysis. Since multivariate regression involves multi-dimensional response variables, it is more strongly affected by the so-called curse of dimension that univariate regression. To overcome this issue, Yoo (2018) and Yoo (2019a) proposed three model-based response dimension reduction methodologies. According to various numerical studies in Yoo (2019a), the default method suggested in Yoo (2019a) is least sensitive to the simulated models, but it is not the best one. To release this issue, the paper proposes an selection algorithm by comparing the other two methods with the default one. This approach is called principal selected response reduction. Various simulation studies show that the proposed method provides more accurate estimation results than the default one by Yoo (2019a), and it confirms practical and empirical usefulness of the propose method over the default one by Yoo (2019a).

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.2 s.2
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• pp.115-121
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• 2001

The purpose of the synthetic generation of monthly river flows based on the short term observed data by means of stochastic models is to provide abundant input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. In this study, a multivariate autoregressive model has been applied to generate monthly flows of the multi sites considering the correlations between each site. The model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, variance, skewness and correlation coefficients. The results of this study showed that the modeled generated flows were statistically similar to the historical flows.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1219-1228
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• 2014

Constant conditional correlation (CCC) is frequently employed for parsimony in the field of multivariate GARCH time series. An extended-CCC (ECCC) model is further developed in order to allow interactions between multivariate volatilities. The paper introduces both CCC model and ECCC model to the domestic financial time series. The CCC and ECCC models are fitted and then compared with each other through various multivatiate time series.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.04a
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• pp.366-367
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• 2006

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.99-112
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• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.775-779
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• 2006

강우자료의 구축은 수문해석에 있어 가장 기본적이며 중요한 단계라 할 수 있다. 하지만 수문 관측 자료의 경우 결측치가 존재하여 그에 대한 보정이 필요한 경우가 종종 발생하게 된다. 따라서 수문자료의 분석을 수행하기에 앞서 우선 자료에 대한 검정을 실시하고, 결측치가 존재할 경우는 이를 보정하여 분석을 수행하여야 한다. 본 연구에서는 다변량통계기법의 하나인 다중회귀분석을 이용하여 강우 결측치를 보정하였다. 본 연구에서는 다중공선성과 자기상관에 대하여 고려한 다중회귀모형을 구성하였다. 모형의 구성시 모든 결측지점에 적용이 가능하지 않아 일반성이 떨어짐을 확인 할 수 있었지만, 모형이 구성될 경우 통계적 적합도와 유의수준을 확인 할 수 있는 장점이 있었으며, 다중회귀모형이 구성되는 경우 좋은 보정 결과를 주는 것을 확인 할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1264-1266
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• 2013

다변량 데이터 분석에 주로 사용되는 차원축소 기법 중 하나인 PCA 알고리즘을 직접 구현해보고 기존의 통계분석 프로그램과 그 결과를 비교분석 해보았다. UCI에서 제공하는 유방암 데이터를 이용하여 실험 해본 결과 두 프로그램 모두 같은 주성분을 얻고, Eigenvalue와 variance도 같은 값을 얻었다. 따라서 상용화된 통계패키지를 사용하지 않고도 PCA 알고리즘을 적용하여 차원축소 문제를 해결하고 데이터를 분석 할 수 있다.