The Resource Introduction to highdimensional statistics, Christophe Giraud
Introduction to highdimensional statistics, Christophe Giraud
Resource Information
The item Introduction to highdimensional statistics, Christophe Giraud represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Introduction to highdimensional statistics, Christophe Giraud represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 Summary
 Evergreater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to HighDimensional Statistics is a concise guide to stateoftheart models, techniques, and approaches for handling highdimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of highdimensional statistics, this highly accessible text: Describes the challenges related to the analysis of highdimensional data Covers cuttingedge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to HighDimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for selfstudy
 Language
 eng
 Extent
 1 online resource.
 Contents

 Chapter 1: Introduction
 Chapter 2: Model Selection
 Chapter 3: Aggregation of Estimators
 Chapter 4: Convex Criteria
 Chapter 5: Estimator Selection
 Chapter 6: Multivariate Regression
 Chapter 7: Graphical Models
 Chapter 8: Multiple Testing
 Chapter 9: Supervised Classification
 Isbn
 9781322629537
 Label
 Introduction to highdimensional statistics
 Title
 Introduction to highdimensional statistics
 Statement of responsibility
 Christophe Giraud
 Subject

 Big data
 Boosting
 Datenanalyse
 Dimensional analysis
 Dimensional analysis
 Electronic book
 Hochdimensionale Daten
 Inferenzstatistik
 LassoMethode
 MATHEMATICS  Applied
 MATHEMATICS  Probability & Statistics  General
 Mathematische Modellierung
 Multivariate analysis
 Multivariate analysis
 Statistics
 Statistics
 Statistik
 Big data
 Language
 eng
 Summary
 Evergreater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to HighDimensional Statistics is a concise guide to stateoftheart models, techniques, and approaches for handling highdimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of highdimensional statistics, this highly accessible text: Describes the challenges related to the analysis of highdimensional data Covers cuttingedge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to HighDimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for selfstudy
 Cataloging source
 UKMGB
 http://library.link/vocab/creatorName
 Giraud, Christophe
 Dewey number
 519.5
 Illustrations
 illustrations
 Index
 index present
 Language note
 English
 LC call number
 QC20.7.D55
 LC item number
 G57 2015eb
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Monographs on statistics & applied probability
 Series volume
 139
 http://library.link/vocab/subjectName

 Dimensional analysis
 Multivariate analysis
 Big data
 Statistics
 MATHEMATICS
 MATHEMATICS
 Big data
 Dimensional analysis
 Multivariate analysis
 Statistics
 Boosting
 Datenanalyse
 Hochdimensionale Daten
 Inferenzstatistik
 LassoMethode
 Mathematische Modellierung
 Statistik
 Label
 Introduction to highdimensional statistics, Christophe Giraud
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category

 text
 still image
 Content type code

 txt
 sti
 Content type MARC source

 rdacontent
 rdacontent
 Contents
 Chapter 1: Introduction  Chapter 2: Model Selection  Chapter 3: Aggregation of Estimators  Chapter 4: Convex Criteria  Chapter 5: Estimator Selection  Chapter 6: Multivariate Regression  Chapter 7: Graphical Models  Chapter 8: Multiple Testing  Chapter 9: Supervised Classification
 Control code
 898156656
 Extent
 1 online resource.
 Form of item
 online
 Isbn
 9781322629537
 Lccn
 2015002096
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 http://library.link/vocab/ext/overdrive/overdriveId
 694235
 Specific material designation
 remote
 System control number
 (OCoLC)898156656
 Label
 Introduction to highdimensional statistics, Christophe Giraud
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category

 text
 still image
 Content type code

 txt
 sti
 Content type MARC source

 rdacontent
 rdacontent
 Contents
 Chapter 1: Introduction  Chapter 2: Model Selection  Chapter 3: Aggregation of Estimators  Chapter 4: Convex Criteria  Chapter 5: Estimator Selection  Chapter 6: Multivariate Regression  Chapter 7: Graphical Models  Chapter 8: Multiple Testing  Chapter 9: Supervised Classification
 Control code
 898156656
 Extent
 1 online resource.
 Form of item
 online
 Isbn
 9781322629537
 Lccn
 2015002096
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 http://library.link/vocab/ext/overdrive/overdriveId
 694235
 Specific material designation
 remote
 System control number
 (OCoLC)898156656
Subject
 Big data
 Boosting
 Datenanalyse
 Dimensional analysis
 Dimensional analysis
 Electronic book
 Hochdimensionale Daten
 Inferenzstatistik
 LassoMethode
 MATHEMATICS  Applied
 MATHEMATICS  Probability & Statistics  General
 Mathematische Modellierung
 Multivariate analysis
 Multivariate analysis
 Statistics
 Statistics
 Statistik
 Big data
Genre
Member of
 Monographs on statistics and applied probability (Series), 139
 O'Reilly Safari Learning Platform: Academic edition
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Introductiontohighdimensionalstatistics/CSIM6YELZs/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Introductiontohighdimensionalstatistics/CSIM6YELZs/">Introduction to highdimensional statistics, Christophe Giraud</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>