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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.
- Sales Rank: #1643068 in Books
- Published on: 2014-12-17
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .80" w x 6.10" l, 1.16 pounds
- Binding: Hardcover
- 270 pages
Review
"Introduction to High-Dimensional Statistics by Christophe Giraud succeeds singularly at providing a structured introduction to this active field of research. … it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. … recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research."
―Journal of the American Statistical Association, December 2015
"This is an attractive textbook. It will prove a very useful addition to any library or personal reference collection. … This book achieves well what it sets out to provide, an introduction to the mathematical foundations of high-dimensional statistics. … likely to stand the test of time well."
―International Statistical Review, 83, 2015
"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
About the Author
Christophe Giraud was a student of the �cole Normale Sup�rieure 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 �cole 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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Needs improvement
By MsCurious
Chapter 1 is interesting and readable, but the rest of the book isn't. The author's notation is cryptic and there are few examples. Recent book Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) is much better at explaining sparsity. Another book Foundations of Machine Learning (Adaptive Computation and Machine Learning series) is better in explaining theory in a clear way. This book seems to focus on regression more than classification theory, which sets it apart, but needs to explain things better.
2 of 8 people found the following review helpful.
Written for a wide technical audience, it starts by ...
By Clement J Jackson
Written for a wide technical audience, it starts by highlighting the limitations of the central ;limit theorem when the dimensionality of the underlying population is large, comparable to n, the sample size. It then proceeds to develop the theoretical framework for dealing with high dimensional statistics. Well written and well illustrated.
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