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      빅데이터와 통계학 = Big data and statistics

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      https://www.riss.kr/link?id=A104377959

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      We investigate the roles of statistics and statisticians in the big data era. Definition and application areas of big data are reviewed and statistical characteristics of big data and their meanings are discussed. Various statistical methodologies applicable to big data analysis are illustrated, and two real big data projects are explained.
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      We investigate the roles of statistics and statisticians in the big data era. Definition and application areas of big data are reviewed and statistical characteristics of big data and their meanings are discussed. Various statistical methodologies app...

      We investigate the roles of statistics and statisticians in the big data era. Definition and application areas of big data are reviewed and statistical characteristics of big data and their meanings are discussed. Various statistical methodologies applicable to big data analysis are illustrated, and two real big data projects are explained.

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      참고문헌 (Reference)

      1 IBM, "http://www-01.ibm.com/software/data/bigdata"

      2 International Data Corporation, "Worldwide big data technology and services 2012-2015 forecast" International Data Corporation 2012

      3 Fan, J., "Variable selection via nonconcave penalized likelihood and its oracle properties" 96 : 1348-1360, 2001

      4 Gill, P. E, "User’s guide for SNOPT 5.3: A Fortran package for large-scale nonlinear programming" University of California 1997

      5 Suchard, M. A., "Understanding GPU programming for statistical computation : Studies in massively parallel massive mixtures" 19 : 419-348, 2010

      6 National Information Society Agency, "Top-10 globally advanced case study of : Big data lead the world" National Information Society Agency 2012

      7 Tibshirani, R. J., "The solution path of the generalized lasso" 39 : 1335-1371, 2011

      8 Liu, H., "The nonparanormal : Semiparametric estimation of high dimensional undirected graphs" 10 : 2295-2328, 2009

      9 Hastie, T., "The entire regularization path for the support vector machine" 5 : 1391-1415, 2004

      10 Van der Laan, M., "Targeted learning: Causal inference for observational and experimental data" Springer 2011

      1 IBM, "http://www-01.ibm.com/software/data/bigdata"

      2 International Data Corporation, "Worldwide big data technology and services 2012-2015 forecast" International Data Corporation 2012

      3 Fan, J., "Variable selection via nonconcave penalized likelihood and its oracle properties" 96 : 1348-1360, 2001

      4 Gill, P. E, "User’s guide for SNOPT 5.3: A Fortran package for large-scale nonlinear programming" University of California 1997

      5 Suchard, M. A., "Understanding GPU programming for statistical computation : Studies in massively parallel massive mixtures" 19 : 419-348, 2010

      6 National Information Society Agency, "Top-10 globally advanced case study of : Big data lead the world" National Information Society Agency 2012

      7 Tibshirani, R. J., "The solution path of the generalized lasso" 39 : 1335-1371, 2011

      8 Liu, H., "The nonparanormal : Semiparametric estimation of high dimensional undirected graphs" 10 : 2295-2328, 2009

      9 Hastie, T., "The entire regularization path for the support vector machine" 5 : 1391-1415, 2004

      10 Van der Laan, M., "Targeted learning: Causal inference for observational and experimental data" Springer 2011

      11 Cortes, C., "Support-vector networks" 20 : 273-297, 1995

      12 Kolaczyk, E. D., "Statistical analysis of network data" Springer 2009

      13 Tibshirani, R., "Sparsity and smoothness via the fused lasso" 67 : 91-108, 2005

      14 Friedman, J. H., "Sparse inverse covariance estimation with the graphical lasso" 9 : 432-441, 2008

      15 Bickel, P. J., "Some theory for Fisher’s linear discriminant function, naive Bayes’, and some alternatives when there are many more variables than observations" 10 : 989-1010, 2004

      16 Fraiman, R., "Selection of variables for cluster analysis and classification rules" 103 : 1294-1303, 2008

      17 Zou, H., "Regularization and variable selection via the elastic net" 67 : 301-320, 2005

      18 Tibshirani, R., "Regression shrinkage and selection via the lasso" 58 : 267-288, 1996

      19 Nishimoto, S., "Reconstructing visual experiences from brain activity evoked by natural movies" 21 : 1641-1646, 2011

      20 Gorban, A. N, "Principal manifolds for data visualization and dimension reduction" Springer 2007

      21 Friedman, J., "Pathwise coordinate optimization" 1 : 302-332, 2007

      22 Hoffman, M., "Online learning for latent dirichlet allocation" 23 : 856-864, 2010

      23 Zhang, C. H., "Nearly unbiased variable selection under minimax concave penalty" 38 : 894-942, 2010

      24 Efron, B., "Least angle regression" 32 : 407-409, 2004

      25 Park, M. Y., "L1-regularization path algorithm for generalized linear models" 69 : 659-677, 2007

      26 Makoto. S., "Impact of big data" Hanbit Inc. 2013

      27 Teh, Y. W., "Hierarchical dirichlet processes" 101 : 1566-1581, 2006

      28 Lam, C., "Hadoop in action" Manning Publications Co. 2012

      29 Jung J., "Engine of creating value, In New chances in the big data era and strategies" National Information Society Agency 2011

      30 Hastie, T., "Elements of statistical learning, 2nd Edition" Springer 2009

      31 Park. C, "Datamining using R, 2nd edition" Kyohak Publishing Co. 2013

      32 Dempster, A. P, "Covariance selection" 28 : 157-175, 1972

      33 Benjamini, Y., "Controlling the false discovery rate: A practical and powerful approach to multiple testing" 289-300, 1995

      34 Grant, M., "CVX: Matlab software for disciplined convex programming"

      35 Manyika, J., "Big data: The next frontier for innovation, competition, and productivity" McKinsey Global Institute 2011

      36 Werbos, P. J., "Beyond regression: New tools for prediction and analysis in the behavioral sciences" Havard University 1974

      37 Seeger, M., "Bayesian modelling in machine learning: A tutorial review, Probabilistic Machine Learning and Medical Image Processing" Saarland University 2009

      38 Breiman, L., "Bagging predictors" 24 : 123-140, 1996

      39 Bellman. R., "Adaptive control processes: A guided tour" Princeton University Press 1961

      40 Hoefling, H., "A path algorithm for the fused lasso signal approximator" 19 : 984-1006, 2010

      41 Freund, Y., "A decision-theoretic generalization of on-line learning and an application to boosting" 55 : 119-139, 1997

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      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2022 평가 계속평가 신청대상 (등재유지)
      2017-01-01 등재 우수등재학술지 선정 (계속평가)
      2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
      2004-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2003-01-01 등재 등재후보학술지 유지 (등재후보2차) KCI등재후보
      2002-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2001-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.18 1.18 1.07
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      1.01 0.91 0.911 0.35
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