Editors:
- Offers a valuable guide to a broad range of big data analytics with statistics in cross-disciplinary applications
- Shows how to handle high-dimensional problems in big data analytics
- Offers software-hardware co-designs for big data analytics
Part of the book series: Springer Handbooks of Computational Statistics (SHCS)
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Table of contents (21 chapters)
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Front Matter
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Overview
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Front Matter
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Methodology
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Front Matter
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Software
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Front Matter
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About this book
Editors and Affiliations
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Ladislaus von Bortkiewicz Chair of Statistics, C.A.S.E. Center for Applied Statistics & Economics, Humboldt-Universität zu Berlin, Berlin, Germany
Wolfgang Karl Härdle
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Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan
Henry Horng-Shing Lu
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School of Statistics, University of Minnesota, Minneapolis, USA
Xiaotong Shen
About the editors
Wolfgang Karl Härdle is Ladislaus von Bortkievicz Professor of Statistics at the Humboldt University of Berlin and director of C.A.S.E. (Center for Applied Statistics and Economics), director of the Collaborative Research Center 649 "Economic Risk" and also of the IRTG 1792 "High Dimensional Nonstationary Time Series". He teaches quantitative finance and semiparametric statistics. Professor Härdle's research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the International Statistical Institute (ISI) and advisor to the Guanghua School of Management, Peking University, China.
Henry Horng-Shing Lu is Professor at the Institute of Statistics of the National Chiao Tung University, Taiwan and serves as the Vice President of Academic Affairs. He received his Ph.D. in Statistics from Cornell University, NY in 1994. He is an elected member of the International Statistical Institute (ISI). His research interests include statistics, applications and big data analytics. Professor Lu analyzes different types of data by developing statistical methodologies for machine learning with the power of statistical inference and computation algorithms. His findings were published in a wide spectrum of journals and conference papers. He also co-edited the Handbook of Statistical Bioinformatics, published by Springer in 2011.
Xiaotong Shen is John Black Johnston Distinguished Professor at the School of Statistics of the University of Minnesota, MN. He received his Ph.D. in Statistics from the University of Chicago, IL in 1991. He is Fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the American Association for the Advancement of Science (AAAS) as well as an elected member of the International Statistical Institute (ISI). Professor Shen’s areas of interest include machine learning anddata mining, likelihood-based inference, semiparametric and nonparametric models, model selection and averaging. His current research efforts are mainly devoted to the further development of structured learning as well as high-dimensional/high-order analysis. The targeted application areas are biomedical sciences and engineering.
Bibliographic Information
Book Title: Handbook of Big Data Analytics
Editors: Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen
Series Title: Springer Handbooks of Computational Statistics
DOI: https://doi.org/10.1007/978-3-319-18284-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-18283-4Published: 01 August 2018
Softcover ISBN: 978-3-030-13238-5Published: 10 December 2019
eBook ISBN: 978-3-319-18284-1Published: 20 July 2018
Series ISSN: 2197-9790
Series E-ISSN: 2197-9804
Edition Number: 1
Number of Pages: VIII, 538
Number of Illustrations: 38 b/w illustrations, 109 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Data Mining and Knowledge Discovery, Mathematical and Computational Engineering, Statistical Theory and Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences