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Handbook of Big Data Analytics

234,33 € Lieferbar ab 09.01.2018


This essential guide to a broad spectrum of big data analytics in cross-disciplinary applications 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 discovery from big data. The primary audience will be 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.  


Titel: Handbook of Big Data Analytics
Autoren/Herausgeber: Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen (Hrsg.)
Aus der Reihe: Springer Handbooks of Computational Statistics
Ausgabe: 1st ed. 2018

ISBN/EAN: 9783319182834

Format: 23,5 x 15,5 cm
Produktform: Hardcover/Gebunden
Gewicht: 0 g
Sprache: Englisch

Wolfgang Karl Härdle is the Ladislaus von Bortkievicz professor of statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. – the Centre for Applied Statistics and Economics and director of the CRC649 „Economic Risk“ and also of the IRTG 1792 „high dimensional non stationary time series“. He teaches quantitative finance and semi parametric statistical. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected ISI member and advisor to the Guanghua School of Management, Peking University.Henry Horng-Shing Lu is a professor in Institute of Statistics, National Chiao Tung University, Taiwan and serve as the Dean in College of Science currently. He received the PhD degree in Statistics from Cornell University in 1994. He is an elected member of International Statistical Institute. His research interests include statistics, applications and big data analytics. He analyzes different types of data by developing statistical methodologies for machine learning with the power of statistical inference and computation algorithms. The research results have been published in a wide spectrum of journals and conferences. He co-edited the Handbook of Statistical Bioinformatics published by Springer in 2011.

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