RISS 학술연구정보서비스

검색

인기 검색어

    다국어 입력

    http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

    변환된 중국어를 복사하여 사용하시면 됩니다.

    예시)
    • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
    • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
    닫기
    KCI우수등재

    빅데이터와 통계학 = Big data and statistics

    한글로보기

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

    • 0

      상세조회
    • 0

      다운로드
    서지정보 열기
    • 내보내기
    • 내책장담기
    • 공유하기
    • 오류접수
    인용문이 복사되었습니다.

    부가정보

    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.
    번역하기

    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.

    더보기

    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

    더보기

    동일학술지(권/호) 다른 논문

    동일학술지 더보기

    더보기

    분석정보

    View

    상세정보조회

    0

    Usage

    원문다운로드

    0

    대출신청

    0

    복사신청

    0

    EDDS신청

    0

    동일 주제 내 활용도 TOP

    더보기

    주제

    연도별 연구동향

    연도별 활용동향

    연관논문

    연구자 네트워크맵

    공동연구자 (7)

    유사연구자 (20) 활용도상위20명

    인용정보 인용지수 설명보기

    학술지 이력

    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
    더보기

    학술지 인용정보

    학술지 인용정보
    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
    더보기

    이 자료와 함께 이용한 RISS 자료

    나만을 위한 추천자료

    해외이동버튼