• 한국수자원학회논문집
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• 제30권2호
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• pp.155-163
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• 1997

본 연구에서는 낙동강유역 진동, 현풍, 왜관 지점의 연평균 유량자료에 대하여 다변량 추계학적 모형올 적용하여 가뭄특성을 해석하였다. 추계학적 모형으로는 다변량 자기회귀 (MAR) 모형과 다변량 contemporaneous 자기회귀 (MCAR) 모형올 사용하였으며, 잔차계열의 왜곡도 검사, 계열상관도(correlogram) 등의 적합도 검정을 통하여 MCAR(1) 모형과 MAR(1) 모형올 적정 모형으로 선정하였다. 또한 MCAR(1) 모형과 MAR(1) 모형에 의해 모의발생된 자료 모두 실제자료의 기본적인 통계값과 매우 비슷하게 나타났다. 따라서 모의발생된 다양한 크기의 자료를 통하여 산정된 3개 지점의 재현기간별 가뭄특성치, 예를 들변 가뭄기간, 가뭄부족량, 가뭄강도 둥은 비교적 잘 재현된 것으로 판단된다. 위와 같이 산정된 가뭄특성치는 중.장기간 수자원 공급체계를 위한 계획과 설계에 중요한 정보를 제공할 것으로 기대된다.

• Journal of applied mathematics & informatics
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• 제13권1_2호
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• pp.479-485
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• 2003

The influence of observations on the Wilks' lambda test of a linear hypothesis in multivariate regression is investigated using the local influence method. The perturbation scheme of case-weights is considered. A numerical example is given to show the effectiveness of the local influence method in identifying the influential observations.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• 한국식품저장유통학회지
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• 제30권4호
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• pp.573-588
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• 2023

The purpose of this study was to monitor changes in the quality of ginseng and predict its shelf-life. As the storage period of ginseng increased, some quality indicators, such as water-soluble pectin (WSP), CDTA-soluble pectin (CSP), cellulose, weight loss, and microbial growth increased, while others (Na₂CO₃-soluble pectin/NSP, hemicellulose, starch, and firmness) decreased. Principal component analysis (PCA) was performed using the quality attribute data and the principal component 1 (PC1) scores extracted from the PCA results were applied to the multivariate analysis. The reaction rate at different temperatures and the temperature dependence of the reaction rate were determined using kinetic and Arrhenius models, respectively. Among the kinetic models, zeroth-order models with cellulose and a PC1 score provided an adequate fit for reaction rate estimation. Hence, the prediction model was constructed by applying the cellulose and PC1 scores to the zeroth-order kinetic and Arrhenius models. The prediction model with PC1 score showed higher R² values (0.877-0.919) than those of cellulose (0.797-0.863), indicating that multivariate analysis using PC1 score is more accurate for the shelf-life prediction of ginseng. The predicted shelf-life using the multivariate accelerated shelf-life test at 5, 20, and 35℃ was 40, 16, and 7 days, respectively.

• Journal of applied mathematics & informatics
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• 제26권3_4호
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• pp.679-688
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• 2008

In multivariate regression analysis there exist many influence measures on the regression estimates. However it seems to be few of influence diagnostics on test statistics in hypothesis testing. Case-deletion approach is fundamental for investigating influence of observations on estimates or statistics. Tang and Fung (1997) derived single case-deletion of the Wilks' ratio, Lawley-Hotelling trace, Pillai's trace for testing a general linear hypothesis of the regression coefficients in multivariate regression. In this paper we derived more extended form of those measures to deal with joint influence among observations. A numerical example is given to illustrate the effect of joint influence on the test statistics.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.641-649
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• 2017

Multivariate confidence regions were defined using a chi-square distribution function under a normal assumption and were represented with ellipse and ellipsoid types of bivariate and trivariate normal distribution functions. In this work, an alternative confidence region using the multivariate quantile vectors is proposed to define the normal distribution as well as any other distributions. These lower and upper bounds could be obtained using quantile vectors, and then the appropriate region between two bounds is referred to as the quantile confidence region. It notes that the upper and lower bounds of the bivariate and trivariate quantile confidence regions are represented as a curve and surface shapes, respectively. The quantile confidence region is obtained for various types of distribution functions that are both symmetric and asymmetric distribution functions. Then, its coverage rate is also calculated and compared. Therefore, we conclude that the quantile confidence region will be useful for the analysis of multivariate data, since it is found to have better coverage rates, even for asymmetric distributions.

• Nuclear Engineering and Technology
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• 제51권1호
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• pp.45-53
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• 2019

Thermal-hydraulic passive safety systems (PSSs) are incorporated into many advanced reactor designs on the bases of simplicity, economics and inherent safety nature. Several factors among which are the critical parameters (CPs) that influence failure and reliability of thermal-hydraulic (t-h) passive systems are now being explored. For simplicity, it is assumed in most reliability analyses that the CPs are independent whereas in practice this assumption is not always valid. There is need to critically examine the dependency influence of the CPs on reliability of the t-h passive systems at design stage and in operation to guarantee safety/better performance. In this paper, two multivariate analysis methods (covariance and conditional subjective probability density function) were presented and applied to a simple PSS. The methods followed a generalized procedure for evaluating t-h reliability based on dependency consideration. A passively water-cooled steam generator was used to demonstrate the dependency of the identified key CPs using the methods. The results obtained from the methods are in agreement and justified the need to consider the dependency of CPs in t-h reliability. For dependable t-h reliability, it is advisable to adopt all possible CPs and apply suitable multivariate method in dependency consideration of CPs among other factors.

• International Journal of Human Ecology
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• 제14권1호
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• pp.29-39
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• 2013

The study examined how changes in time invested in private tutoring and academic achievement influenced each other through a multivariate latent growth model by using the data from the first to the third year presented in the KYPS. This study identifies not only how changes in the private tutoring experience exerted a direct influence on changes in academic achievement, but also measures what kind of changes in private tutoring and academic achievement had emerged over time. The detailed study results are as follows. First, the analysis of time invested in private tutoring showed that the higher the grades, the greater were the amount of time invested in private tutoring in the case of Korean language study. On the other hand, the results showed that in the case of English and mathematics, the higher the grades, the lesser was the amount of time invested in private tutoring. Second, private tutoring and academic achievement were all in a linear relationship. Third, it was shown that the time invested in private tutoring and academic achievement exerted a negative influence on each other according to the passage of time.

• 대한산업공학회지
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• 제27권3호
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• pp.289-296
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• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• Geomechanics and Engineering
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• 제14권4호
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• pp.315-324
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• 2018

With rapid economic growth, numerous deep excavation projects for high-rise buildings and subway transportation networks have been constructed in the past two decades. Deep excavations particularly in thick deposits of soft clay may cause excessive ground movements and thus result in potential damage to adjacent buildings and supporting utilities. Extensive plane strain finite element analyses considering small strain effect have been carried out to examine the wall deflections for excavations in soft clay deposits supported by diaphragm walls and bracings. The excavation geometrical parameters, soil strength and stiffness properties, soil unit weight, the strut stiffness and wall stiffness were varied to study the wall deflection behaviour. Based on these results, a multivariate adaptive regression splines model was developed for estimating the maximum wall deflection. Parametric analyses were also performed to investigate the influence of the various design variables on wall deflections.