• Title/Summary/Keyword: multivariate analysis

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Multivariate Process Capability Index Using Inverted Normal Loss Function (역정규 손실함수를 이용한 다변량 공정능력지수)

  • Moon, Hye-Jin;Chung, Young-Bae
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.41 no.2
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    • pp.174-183
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    • 2018
  • In the industrial fields, the process capability index has been using to evaluate the variation of quality in the process. The traditional process capability indices such as $C_p$, $C_{pk}$, $C_{pm}$ and $C^+_{pm}$ have been applied in the industrial fields. These traditional process capability indices are mainly applied in the univariate analysis. However, the main streams in the recent industry are the multivariate manufacturing process and the multiple quality characteristics are corrected each other. Therefore, the multivariate statistical method should be used in the process capability analysis. The multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. Hence, the purpose of the study is to develop a more effective multivariate process index ($MC_{pI}$) using the multivariate inverted normal loss function. The multivariate inverted normal loss function has the flexibility for the any type of the symmetrical and asymmetrical loss functions as well as the economic information. Especially, the proposed modeling method for the multivariate inverted normal loss function (MINLF) and the expected loss from MINLF in this paper can be applied to the any type of the symmetrical and asymmetrical loss functions. And this modeling method can be easily expanded from a bivariate case to a multivariate case.

Practical Guide to NMR-based Metabolomics - III : NMR Spectrum Processing and Multivariate Analysis

  • Jung, Young-Sang
    • Journal of the Korean Magnetic Resonance Society
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    • v.22 no.3
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    • pp.46-53
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    • 2018
  • NMR-based metabolomics needs various knowledge to elucidate metabolic perturbation such as NMR experiments, NMR spectrum processing, raw data processing, metabolite identification, statistical analysis, and metabolic pathway analysis regarding technical aspects. Among them, some concepts of raw data processing and multivariate analysis are not easy to understand but are important to correctly interpret metabolic profile. This article introduces NMR spectrum processing, raw data processing, and multivariate analysis.

Comparison Analysis of Multivariate Process Capability Indices (다변량 공정능력지수들의 비교분석)

  • Moon, Hye-Jin;Chung, Young-Bae
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.42 no.1
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    • pp.106-114
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    • 2019
  • Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as $MC_{pm}$, $MC^+_{pm}$ and $MC_{pl}$. These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

Development of Multivariate Analysis System by Using SAS/AF and SCL

  • Han, Sang-Tae;Kang, Hyuncheol;Lee, Seong-Keon;Jang, Myung-Seok;Lee, Duck-Ki;Ryu, Dong-Kyun
    • Communications for Statistical Applications and Methods
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    • v.8 no.2
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    • pp.507-514
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    • 2001
  • In recent years, the development and the embodiment of information analysis system has been sprightly carried out in several fields of study. In this study, as and extension of these studies, we develop a system for multivariate analysis which might be widely used in social and natural sciences. This multivariate analysis system is developed by using multivariate analysis procedures in SAS/STAT software. Also, the system supply users with he environment of GUI(Graphical User Interface), which is constructed with AF(application frame) and SCL(screen control language) of SAS software, in order to help users to use the system with easy.

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A Study on Multivriate Process Capability Index using Quality Loss Function (손실함수를 이용한 다변량 공정능력지수에 관한 연구)

  • 문혜진;정영배
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.25 no.2
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    • pp.1-10
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    • 2002
  • Process capability indices are widely used in industries and quality assurance system. In past years, process capability analysis have been used to characterize process performance on the basis of univariate quality characteristics. However, in actual manufacturing industrial, statistical process control (SPC) often entails characterizing or assessing processes or products based on more than one engineering specification or quality characteristic. Therefore, the analysis have to be required a multivariate statistical technique. This paper introduces to multivariate capability indices and then selects a multivariate process capability index incorporated both the process variation and the process deviation from target among these indices under the multivariate normal distribution. We propose a new multivariate capability index $MC_{pm}^+$ using quality loss function instead of the process variation and this index is compared with the proposed indices when quality characteristics are independent and dependent of each other.

Non-Invasive Plasma Monitoring Tools and Multivariate Analysis Techniques for Sensitivity Improvement

  • Jang, Haegyu;Lee, Hak-Seung;Lee, Honyoung;Chae, Heeyeop
    • Applied Science and Convergence Technology
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    • v.23 no.6
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    • pp.328-339
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    • 2014
  • In this article, plasma monitoring tools and mulivariate analysis techniques were reviewed. Optical emission spectroscopy was reviewed for a chemical composition analysis tool and RF V-I probe for a physical analysis tool for plasma monitoring. Multivariate analysis techniques are discussed to the sensitivity improvement. Principal component analysis (PCA) is one of the widely adopted multivariate analysis techniques and its application to end-point detection of plasma etching process is discussed.

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
    • Korean Journal of Food Science and Technology
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    • v.51 no.3
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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.

Improving Interpretability of Multivariate Data Through Rotations of Artificial Variates

  • Hwang, S.Y.;Park, A.M.
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.2
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    • pp.297-306
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    • 2004
  • It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

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Bivariate regional frequency analysis of extreme rainfalls in Korea (이변량 지역빈도해석을 이용한 우리나라 극한 강우 분석)

  • Shin, Ju-Young;Jeong, Changsam;Ahn, Hyunjun;Heo, Jun-Haeng
    • Journal of Korea Water Resources Association
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    • v.51 no.9
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    • pp.747-759
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    • 2018
  • Multivariate regional frequency analysis has advantages of regional and multivariate framework as adopting a large number of regional dataset and modeling phenomena that cannot be considered in the univariate frequency analysis. To the best of our knowledge, the multivariate regional frequency analysis has not been employed for hydrological variables in South Korea. Applicability of the multivariate regional frequency analysis should be investigated for the hydrological variable in South Korea in order to improve our capacity to model the hydrological variables. The current study focused on estimating parameters of regional copula and regional marginal models, selecting the most appropriate distribution models, and estimating regional multivariate growth curve in the multivariate regional frequency analysis. Annual maximum rainfall and duration data observed at 71 stations were used for the analysis. The results of the current study indicate that Frank and Gumbel copula models were selected as the most appropriate regional copula models for the employed regions. Several distributions, e.g. Gumbel and log-normal, were the representative regional marginal models. Based on relative root mean square error of the quantile growth curves, the multivariate regional frequency analysis provided more stable and accurate quantiles than the multivariate at-site frequency analysis, especially for long return periods. Application of regional frequency analysis in bivariate rainfall-duration analysis can provide more stable quantile estimation for hydraulic infrastructure design criteria and accurate modelling of rainfall-duration relationship.

Multivariate Volatility Analysis via Canonical Correlations for Financial Time Series (정준상관분석을 통한 다변량 금융시계열의 변동성 분석)

  • Lee, Seung Yeon;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.27 no.7
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    • pp.1139-1149
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    • 2014
  • Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.