• 한국멀티미디어학회논문지
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• 제17권7호
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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.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.401-410
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood. Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.137-144
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• 1995

Hierarchical Bayes estimations of exchangeable mean vector of a multivariate Poisson distribution are obtained. Since sophiscated analytic integration procedures are needed, the Laplace method is employed in order tocompute these estimations approximately. An example is presented.

• 대한환경공학회지
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• 제37권3호
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• pp.137-144
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• 2015

본 연구는 다변량 통계기법 중 하나인 주성분분석을 활용하여 실시간으로 수질이상 유무를 판단할 수 있는 경보시스템 개발을 목적으로 하였다. 본 연구에서는 다변량 분석 방법 중 수질항목 간의 상관성을 고려한 주성분 분석 방법을 실시간으로 수질이상 유무를 판단하는 알고리즘에 적용시켰다. K-water에서 제공하는 실제 자료를 이용하여 수질 이상에 대한 실시간 감시 알고리즘의 활용성을 검증하였으며, 집중호우 등과 같은 기후변화에 따른 수질이상에 대해서는 기상청 자료와의 비교를 통해 검증하였다.

• 대한인간공학회지
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• 제29권4호
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.

• 물과 미래
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• 제24권4호
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• pp.59-66
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• 1991

단기간의 실측자료를 이용하여 다변량 추계학적 모형에 의해 월유량 자료를 모의발생 시키는 목적은 수자원 시스템의 운영 조작 방침을 결정하기 위한 풍부한 입력자료를 제공하는데 있다. 본연구에서는 2종류의 다변량 모형(Thomas-Fiering 과 Matalas)을 서로 근접해 있는 두 지점에 적용하여 각각의 모형에 의한 모의 결과의 우수성과 적용가능성을 검토하여 보았으며, 이를 위해 모멘트법과 Fourier 분석에 의한 실측자료의 통계특성치를 구하였으며 비교의 기준으로는 실측치와 모의발생 자료의 통계특성을 이용하였다. 본 연구에 사용한 자료를 이용한 연구분석결과로는 다변량 Matalas 모형이 좀더 좋은 결과를 얻을 수 있었으며 변수추정도 수월함을 보였다.

• Communications for Statistical Applications and Methods
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• 제31권2호
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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.

• 산업경영시스템학회지
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• 제19권38호
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• pp.117-129
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• 1996

This Study intends to analysis what 3 factors, which are indices of Capital, Labor and Distribution, really affect to Value-Added Productivity through Statistical Analysis. For this, We selected 12 indices of Value-Added from the edition of 'Annual report of Korean companies' published in 'Korea Investors Service., Inc', especially in parts of Chemicals and Chemical products of total 85 companies. Using this data, Multivariate Statistical Analysis such as Principal Component Analysis, Factor Analysis, Covariance Structure Analysis is taken for modeling the effect of 3 factor(Labor Productivity, Capital Productivity and the Index of Distribution) on Value-Added Productivity.

• 지구물리와물리탐사
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• 제1권3호
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• pp.170-175
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• 1998

이 연구는 다변량 통계 기법을 이용한 물리검층 자료로부터의 암석물리학상 결정으로 암상을 예측하는 것이다. 기술 통계 분석으로 물리검층 자료의 특성을 파악하고 주성분 분석에 의한 다변량 검층 자료들의 상관도 분석을 통해 변수들을 변환시켜 새로운 변수인 주성분을 구하고 변수들의 차원을 축소한다. 통계적 방법에 의한 주성분 검층 자료의 구획에 의한 효율적 자료 축소와 계산의 효율성을 높여 양질의 해석결과를 얻을 수 있다. 구획된 주성분 검층 자료로부터 계보적 군집 분석에 의해 암석물리학상을 결정한다. 최적 암석물리학상의 수는 전체 변동과 군집내의 변동사이의 비와 코어자료 등에 의해 비교 결정된다. 이 연구에서 개발된 암석물리학상 결정법을 국내대륙붕 물리검층자료에 적용한 결과 결정된 암석물리학상은 시추 코어 및 시추 암편 분석에 의한 암상 구분화와 잘 일치하였다. 이러한 연구는 저류층 특성인자의 신뢰성 있고 정량적인 평가로 유전 개발 및 생산 계획 시 유용한 도구로 활용될 수 있을 것이다.

• Computers and Concrete
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• 제13권2호
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• pp.173-185
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• 2014

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.