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Computational Statistics in Data Science 요약정보 및 구매

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지은이 Richard A. Levine, Hao Helen Zhang
발행년도 2023-03-01
판수 1판
페이지 664
ISBN 9781119561071
도서상태 구매가능
판매가격 75,000원
포인트 0점
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  • Computational Statistics in Data Science
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  • DESCRIPTION

    An essential roadmap to the application of computational statistics in contemporary data science

    In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques.

    Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find:

    • A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas
    • Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning

    Perfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.

  • WALTER PIEGORSCH is Professor of Mathematics at the University of Arizona. He is the Director of Statistical Research & Education at the University’s BIO5 Institute.  He is also a Professor of Mathematics, a Professor of Public Health, and a Member and former Chair of the University’s Graduate Interdisciplinary Program (GIDP) in Statistics.  Dr. Piegorsch studies data science for environmental problems, with emphasis on informatics for environmental hazards and risk assessment. He has written many books for Wiley and is one of the Co-Editors of Wiley's Encyclopedia of Environmetrics and Wiley StatsRef.

    RICHARD A. LEVINE is Professor of Statistics at San Diego State University and Faculty Advisor overseeing the Statistical Modeling Group in SDSU Analytic Studies and Institutional Research. He is former Chair of the SDSU Department of Mathematics and Statistics and past Editor of the Journal of Computational and Graphical Statistics. He is Associate Editor for Statistics of the Notices of the American Mathematical Society and is a fellow of the American Statistical Association.

    HAO HELEN ZHANG is Professor of Mathematics Statistics at the University of Arizona and Chair of the UA Graduate Interdisciplinary Program (GIDP) in Statistics and Data Science. Her research areas include statistical machine learning, nonparametric smoothing, and high-dimensional data analysis. She is Editor-in-chief of STAT and Associate Editor for JASA and JRSSB. She is a Fellow of the American Statistical Association (ASA), Fellow of the Institute of Mathematical Statistics (IMS), and 2019 IMS Medallion Lecturer.

    THOMAS C. M. LEE is Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis. He is a former Chair of the Department of Statistics at the same institution and a past editor of the Journal of Computational and Graphical Statistics. He is an elected fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics.

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  • Computational Statistics in Data Science
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