• 제어로봇시스템학회논문지
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• 제11권6호
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• 간호행정학회지
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• 제21권2호
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• pp.184-192
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• 2015

Purpose: This study was done to assess nursing students' practice of patient safety management (PSM), identify factors affecting PSM and provide basic data to develop education programs to strengthen students' competencies for patient safety. Methods: In this descriptive research the practice of PSM by nursing students was examined and predictive factors were identified. Participants were junior and senior nursing students from 7 universities in 7 cities. Self-report questionnaires were used for data collection. Results: Significant positive correlations were found between knowledge of PSM, perception of the importance of PSM and practice of PSM. In multivariate analysis, women students, participation in patient safety education in school, knowledge of PSM, and practice of PSM predicted high perception of the importance of PSM. In multivariate analysis, senior year and participation in patient safety education in school predicted higher knowledge of PSM. In multivariate analysis, perception of the importance of PSM predicted high practice of PSM. Conclusion: In this study, knowledge was not found to directly affect PSM practice, but was found to affect the perception of the importance of PSM, a significant predictive variable. Thus, the importance of PSM should be strongly emphasized during education.

• 한국소음진동공학회논문집
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• 제13권8호
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• pp.583-590
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• 2003

For the preestimate of the vibration level of the ground next to a dwelling, a multivariate statistical analysis on the experiment data acquired from a variety of construction sites was performed, and then a new estimate model for the value of K and n that can be applied in the diagnosis of the damage was offered. The results maybe summarized as follows : First, the $K_{95}$ and n showed high correlation at P$\leq$0.05. Specially the correlation coefficient about $W_{max}$, S were higher in $K_{95}$ than in n. indicating that $K_{95}$ is generally associated with source conditions. Second, the factor analysis permitted to identify two major sources in each fraction. These sources accounted for at least 73 % of valiance of $K_{95}$. Third, the multiple regression model for the estimate of $K_{95}$ was developed from Fac1 which depend upon the source conditions and Fac2 which depend upon the transmission conditions. The n value is able to determine from the correlation relationship associated with $K_{95}$./.

• 응용통계연구
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• 제13권2호
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• pp.343-352
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• 2000

K-평균 군집화(K-means clustering)는 비계층적 군집화 방법이 하나로서 큰 자료에서 개체 군집화에 효율적인 것으로 알려져 있다. 그러나 종종 비교적 균일한 대군집의 일부를 소군집에 떼어주는 오류를 범하기도 한다. 이 연구에서는 그러한 현상을 정확히 인지하고 이에 대한 대책으로서 ‘이중 K-평균 군집화(double K-means clustering)’방법을 제시한다. 또한 실증적 사례에 새 방법론을 적용해보고 토의한다.

• 경영과학
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• 제32권2호
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• pp.69-78
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• 2015

This paper considers a Bayesian Poisson model for multivariate count data using multiplicative rates. More specifically we compose the parameter for overall arrival rates by the product of two parameters, a common effect and an individual effect. The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series. In addition, analysis on individual effects allows the researcher to differentiate the time series by whatevercharacterization of their choice. This type of model allows the researcher to specifically analyze two different forms of effects separately and produce a more robust result. We illustrate a simple MCMC generation combined with a Gibbs sampler step in estimating the posterior joint distribution of all parameters in the model. On the whole, the model presented in this study is an intuitive model which may handle complicated problems, and we highlight the properties and possible applications of the model with an example, analyzing real time series data involving customer arrivals to a large retail store.

• 천문학회보
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• 제36권2호
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• pp.131.2-131.2
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• 2011

We present performance of artificial neural network multivariate classifier in identifying non-astrophysical origin noise transients from the gravitational wave channel of Laser Interferometer Gravitational-wave Observatory (LIGO). LIGO has successfully conducted six science runs, achieving the sensitivity as planned and producing many fruitful scientific results. It has been well observed that the detector noise is non-Gaussian and non-stationary, which results in large excess of noise transients called glitches arising from instrumental and environmental artifacts. Great efforts have been committed to reduce the glitches by tuning the detector instruments and by vetoing them but further improvement is still needed. To this end, there have been efforts to incorporate data from hundreds of auxiliary, physical and environmental channels into identifying the glitches in the gravitational wave channel. We introduce a multivariate classification method using Artificial Neural Networks (ANNs) that efficiently handles large number of variables. In this poster, we present preliminary results of the application of our ANN algorithm to data from LIGO's Science Run 4 and compare its performance with conventional vetoing method.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1333-1343
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• 2014

본 연구는 일변량 금융지수의 변동성 모형에서 GARCH(1,1) 모형이 여러 복잡한 GARCH 확장 모형에 비교해서 결코 뒤쳐지지 않는다는 Hansen과 Lunde (2005) 연구를 다변량 변동성으로 확장한다. 또한 모형의 비교 방법으로 예측값에 기반한 평균제곱예측오차 (MSPE) 뿐 만 아니라 리스크 관리 측면에서 최대 손실 금액을 나타내는 VaR 및 사후 검정인 실패율을 동시에 고려하였다. 모의실험 결과 다변량 변동성의 경우에서도 GARCH 모형이 예측력은 크게 다르지는 않았으나 리스크 관리 측면에서는 좀 더 신중한 판단을 요구함을 보인다. 또한 최근 10년동안의 KOSPI, NASDAQ 및 HANG SENG의 주가 지수 실증 자료를 통하여 리스크 관리 측면에서의 다변량 GARCH 모형 선택에 대해서 논의한다.

• Environmental Engineering Research
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• 제19권3호
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• pp.197-203
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• 2014

This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

• 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.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.305-315
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• 2021

This article aims to explore the contribution of social media value to a company's financial performance in a digital environment economy since the awareness of companies and investors in the use of social media opens up new mechanisms for disseminating information. Quantitative method is used in this study with Multivariate Analysis of Variance as the analysis tool. The data used is secondary data gathered from Indonesia Stock Exchange (IDX) using 308 companies as samples. In the multivariate test, four kinds of multivariate significance tests were carried out, namely Pillai Trace, Wilk Lambda, Hotelling's Trace, and Roy's Largest Root. It was found that social media value has a small contribution in the difference of the level of profitability and the value of the company in Indonesia, but it doesn't have a contribution to the difference of the level of liquidity. The contribution was an implication of online Word of Mouth (WOM) motives which are interrelated with signal theory and as additional information for investors in relation to single-person decision theory. This study provides an insight into the importance of social media management considering that the world of digital economy will continue to develop, so companies in Indonesia need to take advantage of these opportunities.