• Journal of the Korean Institute of Gas
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• v.10 no.4 s.33
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• pp.29-33
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• 2006

Estimation of process safety is important in the chemical process design. Prediction for flash points of flammable substances used in chemical processes is the one of the methods for estimating process safety. Flash point is the property used to examine the potential for the fire and explosion hazards of flammable substances. In this paper, multivariate statistical analysis methods(partial least squares(PLS) quadratic partial least squares(QPLS)) using experimental data is suggested for predicting flash points of flammable substances of binary systems. The prediction results are compared with the values calculated by laws of Raoult and Van Laar equation.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.605-618
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• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

• Water for future
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• v.23 no.1
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• pp.99-107
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• 1990

The statistical analysis based on the past hydrologic data required to set up the water resources development plan and design the hydraulic structres rationally. Because hydrologic events have random factors implied, the sotchastic analysis is necessary. In this paper, same order of stochastic models of monthly runoff data(multivariate AR(1) and AR(2) models, univariate AR(1) and AR(2) models) are applied to compare the statistical characteristics. The other purpose of this paper is to compare the monthly series, which is generated by univariate and multivariate models. By comparing and estimating of each simulated series, it is known that the multivariate models, including the time and spatial colinearity, are better in prediction than univariate models in the analysis of monthly flow at south Han river basin.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.797-805
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• 2005

Multivariate unit root tests for the VAR(p) model have been commonly used in time series analysis. Several unit root tests were developed and recently Shin(2004) suggested a cointegration test based on weighted symmetric estimator. In this paper, we suggest a multivariate unit root test statistic based on the weighted symmetric estimator. Using a small simulation study, we compare the powers of the new test statistic with the statistics suggested in Shin(2004) and Fuller(1996).

• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.45-55
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• 1999

Knowing the number of unit roots is important in the analysis of k-dimensional multivariate autoregressive process. In this paper we suggest simple multiple unit roots test statistics based on least squares estimator for the multivariate AR(1) process in which some eigenvalues are one and the rest are less than one in magnitude. The empirical distributions are tabulated for suggested test statistics. We have small Monte-Calro studies to compare the powers of the test statistics suggested by Johansen(1988) and in this paper.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.939-946
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• 1999

We propose methods for detecting influential observations that have a large influence on the likelihood ratio test statistic for the multivariate Behrens-Fisher problem. For this purpose we derive the influence curve and the derivative influence of the likelihood ratio test statistic. An illustrative example is given to show the effectiveness of the proposed methods on the identification of influential observations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.18-27
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• 2019

The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.535-546
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• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.