High-dimensional statistics / Christophe Giraud, Universite Paris-Sud, France.

Format:

Book

Author/Creator:

Giraud, Christophe, author.

Series:

Monographs on statistics and applied probability (Series) ; 139.

Monographs on statistics and applied probability ; 139

Language:

English

Subjects (All):

Dimensional analysis.

Multivariate analysis.

Big data.

Statistics.

Physical Description:

1 online resource (270 p.)

Edition:

1st edition

Place of Publication:

Boca Raton : CRC Press, [2015]

Language Note:

English

System Details:

text file

Summary:

Ever-greater 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 High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional 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 high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge 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 High-Dimensional 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 self-study.

Contents:

Front Cover; Contents; Preface; Acknowledgments; 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; Appendix A: Gaussian Distribution; Appendix B: Probabilistic Inequalities; Appendix C: Linear Algebra; Appendix D: Subdifferentials of Convex Functions; Appendix E: Reproducing Kernel Hilbert Spaces; Notations; Bibliography; Back Cover

Notes:

A Chapman and Hall Book.

Includes bibliographical references and index.

Online resource; Title from title page (viewed December 17, 2014)

ISBN:

0-429-17389-X

1-4822-3795-4

9780429173899

OCLC:

906192987