Overview

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

Full Product Details

9781322629537

Table of Contents

Reviews

There is a real need for this book. It can quickly make someone new to the field familiar with modern topics in high-dimensional statistics and machine learning, and it is great as a textbook for an advanced graduate course. Marten H. Wegkamp, Cornell University, Ithaca, New York, USA As a mathematician, I am quite charmed by the book and its focus on getting the important ideas through in as short a form as possible, all the while sacrificing none of the mathematical correctness. I certainly plan to use it myself as a support in my own lectures! Gilles Blanchard, University of Potsdam, Germany

Author Information

Christophe Giraud was a student of the Ecole Normale Superieure de Paris, and he received a Ph.D in probability theory from the University Paris 6. He was assistant professor at the University of Nice from 2002 to 2008. He has been associate professor at the Ecole Polytechnique since 2008 and professor at Paris Sud University (Orsay) since 2012. His current research focuses mainly on the statistical theory of high-dimensional data analysis and its applications to life sciences.

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