Computational and statistical methods for analysing big data with applications /

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to...

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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Liu, Shen (Author), McGree, James (Author), Ge, Zongyuan (Author), Xie, Yang (Author)
Format: Electronic eBook
Language:Inglés
Published: London : Academic Press, [2016]
Subjects:
Big data.
Quantitative research.
Quantitative research > Statistical methods.
Data mining > Statistical methods.
Donn�ees volumineuses.
Recherche quantitative.
Recherche quantitative > M�ethodes statistiques.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Quantitative research > Statistical methods
Data mining > Statistical methods
Quantitative research
Big data
Online Access:Texto completo
Description
Summary:Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.
Item Description:"Academic Press is an imprint of Elsevier."
Physical Description:1 online resource (viii, 194 pages) : illustrations (some color)
Bibliography:Includes bibliographical references and index.
ISBN:9780081006511
0081006519

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