• 제목/요약/키워드: Multivariate Statistical Analyses

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Multivariate statistical analysis of the comparative antioxidant activity of the total phenolics and tannins in the water and ethanol extracts of dried goji berry (Lycium chinense) fruits

  • Kim, Joo-Shin;Kimm, Haklin Alex
    • 한국식품과학회지
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    • 제51권3호
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    • pp.227-236
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    • 2019
  • Antioxidant activity in water and ethanol extracts of dried Lycium chinense fruit, as a result of the total phenolic and tannin content, was measured using a number of chemical and biochemical assays for radical scavenging and inhibition of lipid peroxidation, with the analysis being extended by applying a bootstrapping statistical method. Previous statistical analyses mostly provided linear correlation and regression analyses between antioxidant activity and increasing concentrations of phenolics and tannins in a concentration-dependent mode. The present study showed that multiple component or multivariate analysis by applying multiple regression analysis or regression planes proved more informative than linear regression analysis of the relationship between the concentration of individual components and antioxidant activity. In this paper, we represented the multivariate analysis of antioxidant activities of both phenolic and tannin contents combined in the water and ethanol extracts, which revealed the hidden observations that were not evident from linear statistical analysis.

Resistant Singular Value Decomposition and Its Statistical Applications

  • Park, Yong-Seok;Huh, Myung-Hoe
    • Journal of the Korean Statistical Society
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    • 제25권1호
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    • pp.49-66
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    • 1996
  • The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.

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A Review of the Statistical Analysis used in Clinical Articles Published on Journal of Korean Neurosurgical Society

  • Kang, Wee-Chang
    • Journal of Korean Neurosurgical Society
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    • 제40권4호
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    • pp.304-308
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    • 2006
  • Statistical analyses used in clinical articles published on the Journal of Korean Neurosurgical Society were identified and appropriateness of statistical aspects in reporting results was assessed. Forty seven clinical articles were selected in this study, which were published from February, 2005 to February, 2006 on the journal. The frequency of statistical analysis was as follows : descriptive statistics only 24 [51.1%]. one type of statistical method 10 [21.3%], two or more methods 13 [27.6%]. An assessment of statistical aspects was performed in 24 clinical articles reporting inferential statistics. Ten articles [41.7%] did not adequately describe or reference all statistical methods used. There were six articles [25.0%] not reporting the confidence level used as the critical criteria of the statistical significance. In thirteen articles [54.2%] it seems more appropriate to implement multivariate analyses in addition to univariate analyses. We recommend that the journal readers should concentrate on improving their knowledge of basic statistics and statistical review for manuscripts submitted should be sought from professionals in the fields of biostatistics and epidemiology.

Contribution of Ecological Surveys to Coastal Conservation: A Case in Soft Shore Study

  • Tai, K. K;Cheung, S.-G;Shin, P.-K.-S.
    • The Korean Journal of Ecology
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    • 제27권3호
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    • pp.127-131
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    • 2004
  • Soft shores are particularly vulnerable to human exploitation; however, they exhibit a variety of habitats which provide refuge for a diversity of flora and fauna. This study describes a survey of 13 soft shores in Hong Kong with information on species diversity, sediment characteristics, shore extent, pollution threat, degree of naturalness, linkage with other ecological habitats, and degree of social/economic importance. Data collected were subjected to multivariate statistical analyses, so as to identify shores that have significant ecological status and conservation value for management purposes.

주성분분석에 의한 결손 자료의 영향값 검출에 대한 연구 (Detecting Influential Observations in Multivariate Statistical Analysis of Incomplete Data by PCA)

  • 김현정;문승호;신재경
    • 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.

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보건조사연구에서 다변량결측치가 내포된 자료를 효율적으로 분석하기 위한 통계학적 방법 (Statistical Methods for Multivariate Missing Data in Health Survey Research)

  • 김동기;박은철;손명세;김한중;박형욱;안재형;임종건;송기준
    • Journal of Preventive Medicine and Public Health
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    • 제31권4호
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    • pp.875-884
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    • 1998
  • Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the EM algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which uses only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.

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다변수통계방법을 이용한 산지분류에 관한 연구 (A Study on Forest Land Classification Using Multivariate Statistical Methods : A Case Study at Mt. Kwanak)

  • 정순오
    • 한국조경학회지
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    • 제13권1호
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    • pp.43-66
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    • 1985
  • Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

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Moments calculation for truncated multivariate normal in nonlinear generalized mixed models

  • Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • 제27권3호
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    • pp.377-383
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    • 2020
  • The likelihood-based inference in a nonlinear generalized mixed model often requires computing moments of truncated multivariate normal random variables. Many methods have been proposed for the computation using a recurrence relation or the moment generating function; however, these methods rely on high dimensional numerical integrations. The numerical method is known to be inefficient for high dimensional integral in accuracy. Besides the accuracy, the methods demand too much computing time to use them in practical analyses. In this note, a moment calculation method is proposed under an assumption of a certain covariance structure that occurred mostly in generalized mixed models. The method needs only low dimensional numerical integrations.

A rolling analysis on the prediction of value at risk with multivariate GARCH and copula

  • Bai, Yang;Dang, Yibo;Park, Cheolwoo;Lee, Taewook
    • Communications for Statistical Applications and Methods
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    • 제25권6호
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    • pp.605-618
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    • 2018
  • Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

다변량통계기법을 이용한 지하저장시설 주변의 지하수질 변동에 관한 연구 (Use of Multivariate Statistical Approaches for Decoding Chemical Evolution of Groundwater near Underground Storage Caverns)

  • 이정훈
    • 한국지구과학회지
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    • 제35권4호
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    • pp.225-236
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    • 2014
  • 다변량통계기법은 수리지구화학 자료의 분석 및 해석에 많이 이용되어 왔다. 본 연구에서 대응분석과 주성분분석을 동시에 사용하여 인위적인 활동에 의한 지하수의 특징을 살펴보았다. 본 연구의 목적은 NETPATH 프로그램 속의 WATEQ4F를 이용하여 지하수 화학성분의 분화를 계산하고 이를 다변량통계기법을 이용하여 지구화학적인 정보를 추출하는 것이다. 연구지역은 한반도의 남동쪽에 위치한 울산의 LPG 저장시설이다. 본 연구지역에서는 다른 저장시설에서 관찰되는 초염기성의 조성을 가지는 지하수가 관찰되었다. 이러한 인위적인 영향에 의한 높은 pH를 가지는 지하수로 인해 Al의 분화특징과 탄산염의 침전을 유발할 수 있다. 본 연구에서는 연구지역에 지하수에 영향을 주는 두 인위적인 요소(세정작용와 시멘트영향)에 의해서 수리지구화학적인 특징과 상이 어떻게 변하는 가에 초점을 두었다. 이전 연구결과와 두 통계분석을 통해 제시된 결과를 비교하여 지구화학적인 정보를 이용한 주성분분석과 대응분석인 수리지구화학 연구에서 기초연구로 활용될 수 있음을 알 수 있다.