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Handbook of big data analytics

Hardle, Wolfgang Karl.

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Handbook of big data analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Handbook of big data analytics/ edited by Wolfgang Karl Hardle, Henry Horng-Shing Lu, Xiaotong Shen.
other author:	Hardle, Wolfgang Karl.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	viii, 538 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Big data - Statistical methods. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-18284-1
ISBN:	9783319182841

Handbook of big data analytics
Handbook of big data analytics[electronic resource] /edited by Wolfgang Karl Hardle, Henry Horng-Shing Lu, Xiaotong Shen. - Cham :Springer International Publishing :2018. - viii, 538 p. :ill., digital ;24 cm. - Springer handbooks of computational statistics,2197-9790. - Springer handbooks of computational statistics..

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.

ISBN: 9783319182841

Standard No.: 10.1007/978-3-319-18284-1doiSubjects--Topical Terms:

2121460
Big data
--Statistical methods.

LC Class. No.: QA76.9.B45 / H363 2018

Dewey Class. No.: 005.7

Handbook of big data analytics
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245 0 0 $a Handbook of big data analytics $h [electronic resource] / $c edited by Wolfgang Karl Hardle, Henry Horng-Shing Lu, Xiaotong Shen.
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490 1 $a Springer handbooks of computational statistics, $x 2197-9790
520 $a 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.
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