• 제목/요약/키워드: multivariate data analysis

검색결과 1,402건 처리시간 0.033초

자연산 어류의 시장 통합성 분석 (Analyzing Market Integration of Wild Caught Fish Species)

  • 김도훈
    • 수산경영론집
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    • 제44권1호
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    • pp.71-79
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    • 2013
  • This study is aimed to estimate market integration of wild caught fish species on the Korean market, using both multivariate and bivariate cointegration analysis. For the analysis of market integration between wild caught fish species, major four fish species those are most popular fish in the market and caught by the large purse seine fishery-chub mackerel, jack mackerel, hairtail and spanish mackerel-were selected as analytical target fish species. And their real monthly price data from January 2000 to December 2011 were used in the analysis. The results of the multivariate cointegration test for four wild caught fish species showed that there would be long-term equilibrium relationships among prices of four wild caught fish species, and consequently, the markets for wild caught fish species were estimated to be integrated. The results of exclusion test and bivariate cointegration test also supported that there would be a clear evidence to suggest that all target wild caught fish species were cointegrated each other.

High-resolution 1H NMR Spectroscopy of Green and Black Teas

  • Jeong, Ji-Ho;Jang, Hyun-Jun;Kim, Yongae
    • 대한화학회지
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    • 제63권2호
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    • pp.78-84
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    • 2019
  • High-resolution $^1H$ NMR spectroscopic technique has been widely used as one of the most powerful analytical tools in food chemistry as well as to define molecular structure. The $^1H$ NMR spectra-based metabolomics has focused on classification and chemometric analysis of complex mixtures. The principal component analysis (PCA), an unsupervised clustering method and used to reduce the dimensionality of multivariate data, facilitates direct peak quantitation and pattern recognition. Using a combination of these techniques, the various green teas and black teas brewed were investigated via metabolite profiling. These teas were characterized based on the leaf size and country of cultivation, respectively.

Effects of Two Chemotherapy Regimens, Anthracycline-based and CMF, on Breast Cancer Disease Free Survival in the Eastern Mediterranean Region and Asia: A Meta-Analysis Approach for Survival Curves

  • Zare, Najaf;Ghanbari, Saeed;Salehi, Alireza
    • Asian Pacific Journal of Cancer Prevention
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    • 제14권3호
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    • pp.2013-2017
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    • 2013
  • Background: To compare the effects of two adjuvant chemotherapy regimens, anthracycline-based and cyclophosphamide, methotrexate, fluorourical (CMF) on disease free survival for breast cancer patients in the Eastern Mediterranean region and Asia. Methods: In a systematic review with a multivariate mixed model meta-analysis, the reported survival proportion at multiple time points in different studies were combined. Our data sources were studies linking the two chemotherapy regimens on an adjuvant basis with disease free survival published in English and Persian in the Eastern Mediterranean region and Asia. All survival curves were generated with Graphdigitizer software. Results: 14 retrospective cohort studies were located from electronic databases. We analyzed data for 1,086 patients who received anthracycline-based treatment and 1,109 given CMF treatment. For determination of survival proportions and time we usesb the transformation Ln (-Ln(S)) and Ln (time) to make precise estimations and then fit the model. All analyses were carried out with STATA software. Conclusions: Our findings showed a significant efficacy of anthracycline-based adjuvant therapy regarding disease free survival of breast cancer. As a limitation in this meta-analysis we used studies with different types of anthracycline-based regimens.

Analysis of Hyperspectral Dentin Data Using Independent Component Analysis

  • Jung, Sung-Hwan
    • 한국멀티미디어학회논문지
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    • 제12권12호
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    • pp.1755-1760
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    • 2009
  • In this research, for the first time, we tried to analyse Raman hyperspectral dentin data using Independent Component Analysis (ICA) to see its possibility of adoption for the dental analysis software. We captured hyperspectral dentin data on 569 spots on a molar with dental lesion by HR800 Micro Raman Spectrometer at UMKC-CRISP (University of Missouri at Kansas City-Center for Research on Interfacial Structure and Properties). Each spot has 1,005 hyperspectral data. We applied ICA to the captured hyperspectral data of dentin for evaluating ICA approach, and compared it with the well known multivariate analysis method, PCA. As a result of the experiment, ICA approach shows better local characteristic of dentin than the result of PCA. We confirmed that ICA also could be a good method along with PCA in the dental analysis software.

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함수형 ARCH 분석 및 다변량 변동성을 통한 일중 로그 수익률 시간 간격 선택 (Functional ARCH analysis for a choice of time interval in intraday return via multivariate volatility)

  • 김다희;윤재은;황선영
    • 응용통계연구
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    • 제33권3호
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    • pp.297-308
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    • 2020
  • 본 논문에서는 고빈도 함수적 ARCH 모형을 소개하고 근사모형으로써 다변량 변동성 모형을 고려하였다. 이를 기반으로 함수형 변동성 분석에서 중요한 요소인 일중 로그 수익률의 적절한 시간 간격을 찾아보았다. 또한 함수적 ARCH 모형에서 l-시차 후 변동성 예측식을 제시하고 고빈도 KOSPI 자료에 적합하여 예시하였다.

Statistical analysis of metagenomics data

  • Calle, M. Luz
    • Genomics & Informatics
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    • 제17권1호
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    • pp.6.1-6.9
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    • 2019
  • Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. The development of high-throughput sequencing technologies has boosted microbiome research through the study of microbial genomes and allowing a more precise quantification of microbiome abundances and function. Microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional nature. In this review we outline some of the procedures that are most commonly used for microbiome analysis and that are implemented in R packages. We place particular emphasis on the compositional structure of microbiome data. We describe the principles of compositional data analysis and distinguish between standard methods and those that fit into compositional data analysis.

Estimation of Genetic Variance Components of Body Size Measurements in Hanwoo (Korean Cattle) Using a Multivariate Linear Model

  • Lee, Jung-Jae;Kim, Nae-Soo
    • Journal of Animal Science and Technology
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    • 제52권3호
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    • pp.167-174
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    • 2010
  • The objectives of this study were to quantify the combination values of the principal components and factors calculated using body measurements of Hanwoo (Korean Cattle) and estimate their heritabilities. The technique of multivariate analysis was used to reduce a large number of variables to a smaller number of new variables and characterize cattle according to body shape. The analyses were performed using 1,979 cattle at 12 months of age and 936 cattle at 24 months of age. The data for the analyses was obtained from progeny tests performed on Korean Cattle for 6 years from 2003 to 2008. The phenotypic correlations among these traits were estimated to range from 0.32 to 0.90 at 12 months of age and from 0.21 to 0.82 at 24 months of age. The first principal components (PC1s) indicated a weighed average of overall body measurements, accounting for 99.91% of the total variation for both periods of test. The two first PCs had positive coefficients for all body measurements. The major sources of PC, such as chest girth (CG), body length (BL), rump height (RH), and wither height (WH) were similar for both test periods. The heritabilities for PC1, the first factor score (FS1), and the second factor score (FS2) were estimated by multivariate REML method. The estimated heritabilities for PC1, FS1, and FS2 were 0.33, 0.38, and 0.40, respectively, at 12 months of age and 0.26, 0.76, and 0.58 at 24 months of age. Further studies are needed to determine whether the heritabilities of FS1 and FS2 at 24 months of age were overestimated.

Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

  • Lee, Ki-Baek;Kim, Ko Keun;Song, Jaeseung;Ryu, Jiwoo;Kim, Youngjoo;Park, Cheolsoo
    • Journal of Electrical Engineering and Technology
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    • 제11권6호
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    • pp.1812-1824
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    • 2016
  • The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

건설투자(建設投資)의 단기예측모형(短期豫測模型) 비교(比較) (Short-term Construction Investment Forecasting Model in Korea)

  • 김관영;이창수
    • KDI Journal of Economic Policy
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    • 제14권1호
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    • pp.121-145
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    • 1992
  • 본고(本稿)에서는 현재의 경제상황을 잘 반영하는 건설투자활동(建設投資活動)의 단기예측모형(短期豫測模型)을 정립하고자 먼저 관련 시계열자료의 안정성(安定性) 여부(與否)와 순환성(循環性), 계절성(季節性)의 특성을 살펴본 후 여러 단기모형의 예측력(豫測力), 정합성(整合性), 설명력(說明力)을 비교 검토했다. 단위근(單位根) 검정(檢定)과 자기상관계수(自己相關係數) 스펙트랄 밀도함수 분석의 결과, 건설관련 시계열자료들이 대체로 단위근(單位根)을 갖지 않음으로써 안정적이고 주기적인 순환변동을 하고 있으며, 시차변수의 설명력이 높은 특성을 나타내었다. 또한 건설투자자료의 특성이 선행지표(先行指標)인 건축허가연면적(建築許可延面積) 및 건설수주액(建設受注額)과 아주 유사하여 건설투자 단기예측에 있어서 두 지표 사이의 시차관계(時差關係) 파악이 중요함을 알 수 있었다. 제(第)III장(章)에서는 단변량(單變量) 시계열모형(時系列模型)으로 ARIMA모형(模型)과 승법선형추세예측모형(乘法線型趨勢豫測模型)을, 다변량(多變量) 시계열모형(時系列模型)으로는 첫째, 선행지표(先行指標)를 이용한 1차자기회귀모형(次自己回歸模型), VAR모형(模型), 둘째 GNP자료를 이용한 거시경제모형의 단순한 축약형모형(縮約型模型)과 VAR모형(模型)을 제시하고 이들을 비교 평가하였다. 이에 따르면 단변량 시계열모형보다는 다변량 시계열모형이 시간이 경과할수록 예측오차(豫測誤差)가 커지지 않는다는 점에서 우수한 것으로 나타났으며, 다변량모형 중에서도 벡터자기회귀모형이 여타 모형보다 절대예측오차평균(絶對豫測誤差平均), 평균자승근(平均自乘根) 퍼센트 오차(誤差), 결정계수(決定係數) 등 모든 면에서 우수한 것으로 평가되었다. 이는 최근 건설투자가 추세에서 벗어난 급증세를 지속하고 있음을 고려할 때 타당한 결론이라 생각된다.

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다변수 분석법에 의한 조선시대 동전의 분류연구 (Multivariate Classification of Choson Coins)

  • 이창근;강형태;고성희
    • 보존과학연구
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    • 통권8호
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    • pp.1-12
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    • 1987
  • Fifty ancient Korean coins originated in Choson dynasty have been determined for 9 elements such as Sn, Fe, As, Ag, Co, Sb, Ir, Ru and Ni by instrumental neutron activation analysis and for 3 elements such as Cu, Pb, and Zn by atomicalsorption spectrometry. Bronze coins originated in early days of the dynasty contain as major constituents Cu, Pb and Sn approximately in the ratio 90 : 4 : 3, where as, those in latter days contain in the ratio 7 : 2 : 0. Brass coins which had begun in 17century contain as major constituents Cu, Zn and Pb approximately in the ratio 7 : 1: 1. The multivariate date have been analyzed for the relation among elemental contents through the variance-covariance matrix. The data have been fur theranalyzed by a principal component mapping method. As the results training set of 8class have been chosen, based on the spread of sample points in an eigenvector plotand archaeolgical data such as age and the office of minting.

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