• 산업경영시스템학회지
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• 제42권1호
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• pp.106-114
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• 2019

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as $MC_{pm}$, $MC^+_{pm}$ and $MC_{pl}$. These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

• Journal of the Korean Statistical Society
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• 제26권3호
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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.

• Communications for Statistical Applications and Methods
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• 제30권4호
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• pp.389-402
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• 2023

Multivariate or clustered failure time data often occur in many medical, epidemiological, and socio-economic studies when survival data are collected from several research centers. If the data are periodically observed as in a longitudinal study, survival times are often subject to various types of interval-censoring, creating multivariate interval-censored data. Then, the event times of interest may be correlated among individuals who come from the same cluster. In this article, we propose a unified linear regression method for analyzing multivariate interval-censored data. We consider a semiparametric multivariate accelerated failure time model as a statistical analysis tool and develop a generalized Buckley-James method to make inferences by imputing interval-censored observations with their conditional mean values. Since the study population consists of several heterogeneous clusters, where the subjects in the same cluster may be related, we propose a generalized estimating equations approach to accommodate potential dependence in clusters. Our simulation results confirm that the proposed estimator is robust to misspecification of working covariance matrix and statistical efficiency can increase when the working covariance structure is close to the truth. The proposed method is applied to the dataset from a diabetic retinopathy study.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.191-202
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• 2024

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

• 한국자기공명학회논문지
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• 제22권3호
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• pp.46-53
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• 2018

NMR-based metabolomics needs various knowledge to elucidate metabolic perturbation such as NMR experiments, NMR spectrum processing, raw data processing, metabolite identification, statistical analysis, and metabolic pathway analysis regarding technical aspects. Among them, some concepts of raw data processing and multivariate analysis are not easy to understand but are important to correctly interpret metabolic profile. This article introduces NMR spectrum processing, raw data processing, and multivariate analysis.

• 응용통계연구
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• 제17권1호
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• pp.165-172
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• 2004

최근 다변량자료 분석과 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. 이러한 연구들의 연장선상에서, 본 연구에서는 사회 각 분야에서 가장 널리 활용되고 있는 사무용 프로그램 인 Excel을 활용하여 시스템을 개발함으로써, 일반 사용자들도 대화식으로 다변량자료 분석을 쉽게 수행할 수 있도록 하였다.

• 한국석유지질학회:학술대회논문집
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• 한국석유지질학회 1998년도 제5차 학술발표회 발표논문집
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• pp.10-14
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• 1998

A systematic methodology is developed for the electrofacies determination from wireline log data using multivariate statistical analysis. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the efficiency and quality of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification matches well to the core and the cutting data with high reliability This methodology for electrofacies classification can be used to define the reservoir characteristics which are helpful to the reservoir management.

• 응용통계연구
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• 제13권2호
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• pp.383-392
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• 2000

1970년대 후반부터 영향력이 있는 관측값을 검출하기 위해서 회귀분석을 포함한 다양한 다변량 해석법에서의 영향분석 및 감도분석에 대한 연구가 진행되어 왔다. 결손 값이 포함된 불완전한 자료에 관해서도 이러한 연구가 필요하다. 이와 관련하여 Kim et al.(1998)등은 평균벡터와 분산공분산행렬에 대한 최우추정값에 초점을 두고 불완전한 자료에 대한 다변량 해석법에서의 감도분석에 관한 방법적 연구를 다루었다. Kim et al.(1998)에서는 Cook’s D 통계량을 이용하였으나, 본 논문에서는 결손값이 있는 다변량 자료에 대해서 주성분을 이용하여 영향력이 있는 관측값을 검출하는 방법에 대해서 살펴보았다. 이 때, 결손값은 EM알고리즘에 의해 대치하여 PCA 통계량을 유도하였다.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.447-456
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• 2007

Recently, the multivariate statistical analysis has been used to analyze meaningful information for various data. In this paper, we develope the multivariate statistical analysis system combined with Fisher discriminant analysis, logistic regression, neural network, and decision tree using visual basic 6.0.

• 대한인간공학회지
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• 제24권4호
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• pp.39-46
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• 2005

The purpose of the study is to suggest a methodology for properly categorizing the body type of Koreans based on the multivariate statistical analysis. Anthropometric data used in the study were measured from the sampled strata of about fifteen thousand Koreans surveyed through the 5th national anthropometic data measurement project called Size Korea funded by ATS, Korea, during 2003-2004. In order to categorize whole body types, the normalized anthropometric variables, being divided by its stature, were used for obtaining a set of factors that supposedly represent body types through the factor analysis. These factors, which were again clustered, yielded the body types according to the gender. The body types classified are expected to be applied to product design for clothing, furniture, automobile packaging, etc.