Handbook of Big Data Analytics

Wolfgang Karl Härdle

ISBN 10: 3030132382 / ISBN 13: 9783030132385

Editore: Springer International Publishing, Springer International Publishing Dez 2019, 2019

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Neuware -Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 548 pp. Englisch.

Codice articolo 9783030132385

Dati bibliografici

• Titolo: Handbook of Big Data Analytics
• Editore: Springer International Publishing, Springer International Publishing Dez 2019
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030132382
• ISBN 13: 9783030132385
• Rilegatura: Taschenbuch
• Condizione: Neu

Descrizione articolo

Riassunto

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.  

Informazioni sull?autore

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.