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The Resource Handbook of Big Data Analytics, edited by Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen, (electronic resource)

Handbook of Big Data Analytics, edited by Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen, (electronic resource)

Editor
Contributor
Summary
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.--
Language
eng
Work
Publication
Extent
1 online resource (VIII, 538 pages)
Contents
  • Preface
  • Statistics, Statisticians, and the Internet of Things (John M. Jordan and Dennis K. J. Lin)
  • Cognitive Data Analysis for Big Data (Jing Shyr, Jane Chu and Mike Woods)
  • Statistical Leveraging Methods in Big Data (Xinlian Zhang, Rui Xie and Ping Ma)
  • Scattered Data and Aggregated Inference (Xiaoming Huo, Cheng Huang and Xuelei Sherry Ni)
  • Nonparametric Methods for Big Data Analytics (Hao Helen Zhang)
  • Finding Patterns in Time Series (James E. Gentle and Seunghye J. Wilson)
  • Variational Bayes for Hierarchical Mixture Models (Muting Wan, James G. Booth and Martin T. Wells)
  • Hypothesis Testing for High-Dimensional Data (Wei Biao Wu, Zhipeng Lou and Yuefeng Han)
  • High-Dimensional Classification (Hui Zou)
  • Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithms (Hsiang-Ling Hsu, Ching-Kang Ing and Tze Leung Lai)
  • Semi-Supervised Smoothing for Large Data Problems (Mark Vere Culp, Kenneth Joseph Ryan and George Michailidis)
  • Inverse Modeling: A Strategy to Cope with Non-Linearity (Qian Lin, Yang Li and Jun S. Liu)
  • Sufficient Dimension Reduction for Tensor Data (Yiwen Liu, Xin Xing and Wenxuan Zhong)
  • Compressive Sensing and Sparse Coding (Kevin Chen and H. T. Kung)
  • Bridging Density Functional Theory and Big Data Analytics with Applications (Chien-Chang Chen, Hung-Hui Juan, Meng-Yuan Tsai and Henry Horng-Shing Lu)
  • Q3-D3-LSA: D3.js and generalized vector space models for Statistical Computing (Lukas Borke and Wolfgang Karl Härdle)
  • A Tutorial on Libra: R Package for the Linearized Bregman Algorithm in High-Dimensional Statistics (Jiechao Xiong, Feng Ruan and Yuan Yao)
  • Functional Data Analysis for Big Data: A Case Study on California Temperature Trends (Pantelis Zenon Hadjipantelis and Hans-Georg Müller)
  • Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging (Martin Bezener, Lynn E. Eberly, John Hughes, Galin Jones and Donald R. Musgrove)
  • Construction of Tight Frames on Graphs and Application to Denoising (Franziska Göbel, Gilles Blanchard and Ulrike von Luxburg)
  • Beta-Boosted Ensemble for Big Credit Scoring Data (Maciej Zięba and Wolfgang Karl Härdle)
Isbn
9783319182841
Link
https://eui.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-18284-1
Instance
Label
Handbook of Big Data Analytics
Title
Handbook of Big Data Analytics
Statement of responsibility
edited by Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen
Contributor
Editor
Subject
Language
eng
Summary
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.--
Member of
Assigning source
Provided by publisher
Image bit depth
0
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Härdle, Wolfgang Karl
  • Lu, Henry Horng-Shing
  • Shen, Xiaotong
Series statement
  • Springer eBooks.
  • Springer eBooks
  • Springer Handbooks of Computational Statistics,
http://library.link/vocab/subjectName
  • Data mining
  • Statistics
Label
Handbook of Big Data Analytics, edited by Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-18284-1
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Preface -- Statistics, Statisticians, and the Internet of Things (John M. Jordan and Dennis K. J. Lin) -- Cognitive Data Analysis for Big Data (Jing Shyr, Jane Chu and Mike Woods) -- Statistical Leveraging Methods in Big Data (Xinlian Zhang, Rui Xie and Ping Ma) -- Scattered Data and Aggregated Inference (Xiaoming Huo, Cheng Huang and Xuelei Sherry Ni) -- Nonparametric Methods for Big Data Analytics (Hao Helen Zhang) -- Finding Patterns in Time Series (James E. Gentle and Seunghye J. Wilson) -- Variational Bayes for Hierarchical Mixture Models (Muting Wan, James G. Booth and Martin T. Wells) -- Hypothesis Testing for High-Dimensional Data (Wei Biao Wu, Zhipeng Lou and Yuefeng Han) -- High-Dimensional Classification (Hui Zou) -- Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithms (Hsiang-Ling Hsu, Ching-Kang Ing and Tze Leung Lai) -- Semi-Supervised Smoothing for Large Data Problems (Mark Vere Culp, Kenneth Joseph Ryan and George Michailidis) -- Inverse Modeling: A Strategy to Cope with Non-Linearity (Qian Lin, Yang Li and Jun S. Liu) -- Sufficient Dimension Reduction for Tensor Data (Yiwen Liu, Xin Xing and Wenxuan Zhong) -- Compressive Sensing and Sparse Coding (Kevin Chen and H. T. Kung) -- Bridging Density Functional Theory and Big Data Analytics with Applications (Chien-Chang Chen, Hung-Hui Juan, Meng-Yuan Tsai and Henry Horng-Shing Lu) -- Q3-D3-LSA: D3.js and generalized vector space models for Statistical Computing (Lukas Borke and Wolfgang Karl Härdle) -- A Tutorial on Libra: R Package for the Linearized Bregman Algorithm in High-Dimensional Statistics (Jiechao Xiong, Feng Ruan and Yuan Yao) -- Functional Data Analysis for Big Data: A Case Study on California Temperature Trends (Pantelis Zenon Hadjipantelis and Hans-Georg Müller) -- Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging (Martin Bezener, Lynn E. Eberly, John Hughes, Galin Jones and Donald R. Musgrove) -- Construction of Tight Frames on Graphs and Application to Denoising (Franziska Göbel, Gilles Blanchard and Ulrike von Luxburg) -- Beta-Boosted Ensemble for Big Credit Scoring Data (Maciej Zięba and Wolfgang Karl Härdle) --
Control code
978-3-319-18284-1
Dimensions
unknown
Extent
1 online resource (VIII, 538 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9783319182841
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
147 illustrations, 109 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1045629779
Label
Handbook of Big Data Analytics, edited by Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-18284-1
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Preface -- Statistics, Statisticians, and the Internet of Things (John M. Jordan and Dennis K. J. Lin) -- Cognitive Data Analysis for Big Data (Jing Shyr, Jane Chu and Mike Woods) -- Statistical Leveraging Methods in Big Data (Xinlian Zhang, Rui Xie and Ping Ma) -- Scattered Data and Aggregated Inference (Xiaoming Huo, Cheng Huang and Xuelei Sherry Ni) -- Nonparametric Methods for Big Data Analytics (Hao Helen Zhang) -- Finding Patterns in Time Series (James E. Gentle and Seunghye J. Wilson) -- Variational Bayes for Hierarchical Mixture Models (Muting Wan, James G. Booth and Martin T. Wells) -- Hypothesis Testing for High-Dimensional Data (Wei Biao Wu, Zhipeng Lou and Yuefeng Han) -- High-Dimensional Classification (Hui Zou) -- Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithms (Hsiang-Ling Hsu, Ching-Kang Ing and Tze Leung Lai) -- Semi-Supervised Smoothing for Large Data Problems (Mark Vere Culp, Kenneth Joseph Ryan and George Michailidis) -- Inverse Modeling: A Strategy to Cope with Non-Linearity (Qian Lin, Yang Li and Jun S. Liu) -- Sufficient Dimension Reduction for Tensor Data (Yiwen Liu, Xin Xing and Wenxuan Zhong) -- Compressive Sensing and Sparse Coding (Kevin Chen and H. T. Kung) -- Bridging Density Functional Theory and Big Data Analytics with Applications (Chien-Chang Chen, Hung-Hui Juan, Meng-Yuan Tsai and Henry Horng-Shing Lu) -- Q3-D3-LSA: D3.js and generalized vector space models for Statistical Computing (Lukas Borke and Wolfgang Karl Härdle) -- A Tutorial on Libra: R Package for the Linearized Bregman Algorithm in High-Dimensional Statistics (Jiechao Xiong, Feng Ruan and Yuan Yao) -- Functional Data Analysis for Big Data: A Case Study on California Temperature Trends (Pantelis Zenon Hadjipantelis and Hans-Georg Müller) -- Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging (Martin Bezener, Lynn E. Eberly, John Hughes, Galin Jones and Donald R. Musgrove) -- Construction of Tight Frames on Graphs and Application to Denoising (Franziska Göbel, Gilles Blanchard and Ulrike von Luxburg) -- Beta-Boosted Ensemble for Big Credit Scoring Data (Maciej Zięba and Wolfgang Karl Härdle) --
Control code
978-3-319-18284-1
Dimensions
unknown
Extent
1 online resource (VIII, 538 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9783319182841
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
147 illustrations, 109 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1045629779

Subject

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