• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.1-13
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• 1992

This article deals with variable selection problem under a newly formed predictive heteroscedastic discriminant rule that accounts for mulitple homogeneous covariance matrices across the K multivariate normal populations. A general version of predictive discriminant rule, a variable selection criterion, and a criterion for stopping with further selection are suggested. In a simulation study the practical utilities of those considered are demonstrated.

• Journal of the Korean Statistical Society
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• v.17 no.1
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• pp.24-29
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• 1988

The asymptotic distribution of the sample partial autocorrelation terms after lag p of an AR(p) model has been shown by Dixon(1944). The derivation is based on multivariate analysis and looks tedious. In this paper we present an interesting contour-integral derivation.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.163-165
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• 2003

Some multivariate analyses provide configurations for variables or objects in low dimensional space because we can see easily their relation. In particular, in simple correspondence analysis(SCA), we can obtain the various configurations which are called SCA Maps based on the algebraic algorithms. Moreover, it often occur the variability among them. Therefore, in this study, we will give a comparison of variability of SCA maps using the procrustes analysis which is a technique of comparing configurations in multidimensional scaling.

• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.503-521
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• 2005

Process capability indices are widely used in industries and quality assurance system. When designing the parameter on the multiple quality characteristics, there has been a study for optimization of problems, but there has been few former study on the possible conflicting phenomena in considertion of the correlations among the characteristics. To solve the issue on the optimal design for muliple quality characteristics, the study propose the expected loss function with cross-product terms among the characteristics and derived range of the coefficients of terms. Therefore, the analysis have to be required a multivariate statistical technique. This paper introduces to multivariate capability indices and then selects a multivariate process capability index incorporated both the process variation and the process deviation from target among these indices under the multivariate normal distribution. We propose a new multivariate capability index $MC_{pm}^{++}$ using quality loss function instead of the process variation and this index is compared with the proposed indices when quality characteristics are independent and dependent of each other,

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.69-79
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• 2004

Process capability indices are widely used in industries and quality assurance system. When designing the parameter on the multiple quality characteristics, there has been a study for optimization of problems, but there has been few former study on the possible conflicting phenomena in considertion of the correlations among the characteristics. To solve the issue on the optimal design for multiple quality characteristics, the study propose the expected loss function with cross-product terms among the characteristics and derived range of the coefficients of terms. Therefore, the analysis have to be required a multivariate statistical technique. This paper introduces to multivariate capability indices and then selects a multivariate process capability index incorporated both the process variation and the process deviation from target among these indices under the multivariate normal distribution. We propose a new multivariate capability index $MC_{pm}^{++}$ using quality loss function instead of the process variation and this index is compared with the proposed indices when quality characteristics are independent and dependent of each other.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.1
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• pp.83-89
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• 1996

Multivariate statistical procedures were used to analyse data on the chemical composition and in vitro digestibility of four varienties of rice straw after treatment with 4% NaOH solution, 4% urea solution or distilled water (control) for 48 hours. For each treatment, stepwise discriminant analysis identified the variables which maximized differences between varieties and the eigenvectors from principal component analysis quantified the contribution of these criterion variables to varietal differences. The overall response of varieties to chemical treatment was demonstrated qualitatively, by cluster analysis, and quantitatively, from the magnitude of the principal component scores. The analysis revealed that the urea and control treatments elicited the same response whereas NaOH had the greatest effect on the poorest straw variety. Similar analyses conducted on the botanical fractions of the varieties showed that the relative response of the inflorescence, stem, leaf blade and leaf sheath fractions was not altered by chemical treatment.

• 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.

• Smart Structures and Systems
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• v.14 no.3
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.

• Water for future
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• v.24 no.4
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• pp.59-66
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• 1991

Abstract The purpose of the synthetic of monthly river flows based on the short-term observed data by means of multivariate stochastic models is to provide abundunt input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. In this study, multivariate Thomas-Fiering and Matalas models for synthetic generation based on stream flows in neihboring basin were employed to check if it can be applide in the modeling of monthly flows. Statistical parameters estimated by Method of Moment and Fourier Series Analysis respectively were reproduced for statistical features. For comparisons the statistical parameters of the generated monthly flow by each model were compared with those of the observed monthly flows. Results of this study suggest that the application of Matalas model for synthetic generation of monthly river flows can be adapted.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.