• Title/Summary/Keyword: multivariate data analysis

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Note on response dimension reduction for multivariate regression

  • Yoo, Jae Keun
    • Communications for Statistical Applications and Methods
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    • v.26 no.5
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    • pp.519-526
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    • 2019
  • Response dimension reduction in a sufficient dimension reduction (SDR) context has been widely ignored until Yoo and Cook (Computational Statistics and Data Analysis, 53, 334-343, 2008) founded theories for it and developed an estimation approach. Recent research in SDR shows that a semi-parametric approach can outperform conventional non-parametric SDR methods. Yoo (Statistics: A Journal of Theoretical and Applied Statistics, 52, 409-425, 2018) developed a semi-parametric approach for response reduction in Yoo and Cook (2008) context, and Yoo (Journal of the Korean Statistical Society, 2019) completes the semi-parametric approach by proposing an unstructured method. This paper theoretically discusses and provides insightful remarks on three versions of semi-parametric approaches that can be useful for statistical practitioners. It is also possible to avoid numerical instability by presenting the results for an orthogonal transformation of the response variables.

A Study on the Urban Spatial Structure - A Case Study of Jinju City - (도시공간구조 분석에 관한 연구 - 진주시를 사례로 -)

  • Cho, Jeong-Hyun;Lee, Chang-Hak;Baek, Tae-Kyung
    • Journal of the Korean Association of Geographic Information Studies
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    • v.14 no.4
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    • pp.92-101
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    • 2011
  • This study analyzed the urban structure of Jinju city where urban doughnut phenomena, development of new town at suburban zone and establishment of innovation city appear. The sphere of this study was set limit to Jinju's dong area due to taking the limitation of data. Multivariate analysis was done by using 24 variables to classify into seven clusters(CBD, Industrial Area, Residential Area etc). We studied regional condition and problems at the relation between analyzed regional features of this study and development principles at the upper planning. Jinju city needs urban redevelopment, reconstruction works and redevelopment promotion project for urban outworn zone in view of the regional conditions to innovate outdated city image and restore western Gyeongnam as a central city and also they should promote innovative city that is progressing now and construction of new town that is linked with Sangpyeong industrial complex removal as well as the whole Chojang-dong zone. In conclusion, this study will help to understand regional phenomenon like regional development project and urban management.

Environmental Evaluation of Fish Aquafarm off Baegyado in Yeosu by Multivariate Analysis (다변량분석에 의한 여수 백야도 어류양식장의 해양 환경분석)

  • LEE, Chang-Hyeok;KANG, Man-Gu;LIM, Su-Yeon;KIM, Jae-Hyun;SHIN, Jong-Ahm
    • Journal of Fisheries and Marine Sciences Education
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    • v.29 no.3
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    • pp.785-798
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    • 2017
  • This study was conducted to evaluated the surface(10 variables) and bottom(10 variables) water quality, and sediment(3 variables) in the cage fish farm off Baegyado in Gamak Bay using a multivariate analysis from January 2013 to November 2014. Generally, the environmental data did not show a certain tendency by months during two years investigated. The pairwise simple correlation matrices among variables were also shown. The first four principal components of the surface water in 2013 explain 93% of the total sample variance; the first principal component($z_1$) showed the freshwater inflow and/or precipitation, $z_2$, $z_3$ and $z_4$ related to freshwater inflow and/or precipitation, organic matters and eutrophy, respectively; the first four principal components of the bottom water in 2013 explain 93% of the total sample variance; the $z_1$, $z_2$ and $z_4$ related to freshwater inflow and/or precipitation, and $z_3$ water temperature. In 2014, at the surface water the first three principal components explain 87%; the $z_1$, $z_2$ and $z_3$ related to water temperature, eutrophy and freshwater inflow and/or precipitation, respectively; at the bottom water the first three principal components explain 93%; $z_1$, $z_2$ and $z_3$ related to water temperature, freshwater inflow and/or precipitation and eutrophy. Half of the principal components related to freshwater inflow and/or precipitation.

Feedwater Flowrate Estimation Based on the Two-step De-noising Using the Wavelet Analysis and an Autoassociative Neural Network

  • Gyunyoung Heo;Park, Seong-Soo;Chang, Soon-Heung
    • Nuclear Engineering and Technology
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    • v.31 no.2
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    • pp.192-201
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    • 1999
  • This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

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Influence of age at complementary food introduction on the development of asthma and atopic dermatitis in Korean children aged 1-3 years

  • Lee, Jihyun;Shin, Meeyong;Lee, Bora
    • Clinical and Experimental Pediatrics
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    • v.64 no.8
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    • pp.408-414
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    • 2021
  • Background: Complementary food in infancy is necessary for human growth, neurodevelopment, and health. However, the role of allergen consumption in early infancy and its effects on the development of food allergy or tolerance remain unclear. Purpose: To investigate the influence of age at the time of complementary food introduction on the development of asthma and atopic dermatitis in Korean children aged 1-3 years. Methods: We combined data from the Korea National Health and Nutrition Examination Survey collected from 2010 to 2014 and analyzed 1619 children aged 1-3 years who were included in the survey. Multivariate regression analysis was used to identify associations among type of feeding, age at the time of complementary food introduction, and doctor-diagnosed atopic dermatitis and asthma. Results: Age at the time of complementary food introduction was not significantly associated with doctor-diagnosed atopic dermatitis and asthma in children aged 1-3 years. In the univariate analysis, children with asthma showed higher water and sodium intake levels than nonasthmatic children. However, this relationship was not significant in the multivariate regression analysis. Conclusion: The present study revealed no statistically significant relationship between age at the time of complementary food introduction and the risk of atopic dermatitis and asthma in young Korean children. A national prospective study is needed to clarify the influence of age at the time of complementary food introduction on the development of allergic diseases.

Multiple imputation and synthetic data (다중대체와 재현자료 작성)

  • Kim, Joungyoun;Park, Min-Jeong
    • The Korean Journal of Applied Statistics
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    • v.32 no.1
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    • pp.83-97
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    • 2019
  • As society develops, the dissemination of microdata has increased to respond to diverse analytical needs of users. Analysis of microdata for policy making, academic purposes, etc. is highly desirable in terms of value creation. However, the provision of microdata, whose usefulness is guaranteed, has a risk of exposure of personal information. Several methods have been considered to ensure the protection of personal information while ensuring the usefulness of the data. One of these methods has been studied to generate and utilize synthetic data. This paper aims to understand the synthetic data by exploring methodologies and precautions related to synthetic data. To this end, we first explain muptiple imputation, Bayesian predictive model, and Bayesian bootstrap, which are basic foundations for synthetic data. And then, we link these concepts to the construction of fully/partially synthetic data. To understand the creation of synthetic data, we review a real longitudinal synthetic data example which is based on sequential regression multivariate imputation.

Classification and Analysis of the Somatotype through Side View Silhouette of the whole body by Multivariate Method (다변량분석법에 의한 측면전신체형 분류)

  • 권숙희
    • Journal of the Korean Society of Clothing and Textiles
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    • v.21 no.7
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    • pp.1227-1235
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    • 1997
  • The purpose of this study was to classify the somatotype based on the side view and to analyze the characteristics of each somatotype. In order to reduce the burden of stocks and increase clothing fitness, systematic information on typical body sizes and somatotypes is essential. The subjects were 206 unmarried women aged from 19-29. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and analysis of variance. As the result of factor analysis for the classification of somatotypes, 8 factors which explain 74.7% of variance were extracted from 35 photometric and 17 anthrometric data. Using factor scores cluster analysis was carried out and the subjects were classified into 4 cluster.Each cluster was classified as bending type, swayback, turning over type and straight type accordding to its position to the relativeplumb line and their side view contour.

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Characterization of Korean Archaeological Artifacts by Neutron Activation Analysis (II). Multivariate Classification of Korean Ancient Glass Pieces (중성자 방사화분석에 의한 한국산 고고학적 유물의 특성화 연구 (II). 다변량 해석법에 의한 고대 유리제품의 분류 연구)

  • Chul Lee;Oh Cheun Kwun;Ihn Chong Lee;Nak Bae Kim
    • Journal of the Korean Chemical Society
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    • v.31 no.6
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    • pp.567-575
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    • 1987
  • Fourty five ancient Korean glass pieces have been determined for 19 elements such as Ag, As, Br, Ce, Co, Cr, Eu, Fe, Hf, K, La, Lu, Na, Ru, Sb, Sc, Sm, Th and Zn, and for one such as Pb by instrumental neutron activation analysis and by atomic absorption spectrometry, respectively. The multivariate data have been analyzed for the relation among elemental contents through the variance-covariance matrix. The data have been further analyzed by a principal component mapping method. As the results training set of 5 class have been chosen, based on the spread of sample points in an eigen vector plot and archaeological data. The 5 training set consisting of 36 species and a test set consisting of 9 species bave finally been analyzed for the assignment to certain classes or outliers through the statistical isolinear multiple component analysis (SIMCA). The results have showed the whole species for 5 training set and 3 species in the test set are assigned appropriately and these are in accord with the results by principal component mapping.

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Evaluation of Occupational, Facility and Environmental Radiological Data From the Centralized Radioactive Waste Management Facility in Accra, Ghana

  • Gustav Gbeddy;Yaw Adjei-Kyereme;Eric T. Glover;Eric Akortia;Paul Essel;Abdallah M.A. Dawood;Evans Ameho;Emmanuel Aberikae
    • Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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    • v.21 no.3
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    • pp.371-381
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    • 2023
  • Evaluating the effectiveness of the radiation protection measures deployed at the Centralized Radioactive Waste Management Facility in Ghana is pivotal to guaranteeing the safety of personnel, public and the environment, thus the need for this study. RadiagemTM 2000 was used in measuring the dose rate of the facility whilst the personal radiation exposure of the personnel from 2011 to 2022 was measured from the thermoluminescent dosimeter badges using Harshaw 6600 Plus Automated TLD Reader. The decay store containing scrap metals from dismantled disused sealed radioactive sources (DSRS), and low-level wastes measured the highest dose rate of 1.06 ± 0.92 µSv·h-1. The range of the mean annual average personnel dose equivalent is 0.41-2.07 mSv. The annual effective doses are below the ICRP limit of 20 mSv. From the multivariate principal component analysis biplot, all the personal dose equivalent formed a cluster, and the cluster is mostly influenced by the radiological data from the outer wall surface of the facility where no DSRS are stored. The personal dose equivalents are not primarily due to the radiation exposures of staff during operations with DSRS at the facility but can be attributed to environmental radiation, thus the current radiation protection measures at the Facility can be deemed as effective.

Principal component analysis for Hilbertian functional data

  • Kim, Dongwoo;Lee, Young Kyung;Park, Byeong U.
    • Communications for Statistical Applications and Methods
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    • v.27 no.1
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    • pp.149-161
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    • 2020
  • In this paper we extend the functional principal component analysis for real-valued random functions to the case of Hilbert-space-valued functional random objects. For this, we introduce an autocovariance operator acting on the space of real-valued functions. We establish an eigendecomposition of the autocovariance operator and a Karuhnen-Loève expansion. We propose the estimators of the eigenfunctions and the functional principal component scores, and investigate the rates of convergence of the estimators to their targets. We detail the implementation of the methodology for the cases of compositional vectors and density functions, and illustrate the method by analyzing time-varying population composition data. We also discuss an extension of the methodology to multivariate cases and develop the corresponding theory.