Concentration Inequalities and Model Selection

Concentration Inequalities and Model Selection
Author: Pascal Massart
Publisher: Springer
Total Pages: 346
Release: 2007-04-26
Genre: Mathematics
ISBN: 3540485031

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Concentration inequalities have been recognized as fundamental tools in several domains such as geometry of Banach spaces or random combinatorics. They also turn to be essential tools to develop a non asymptotic theory in statistics. This volume provides an overview of a non asymptotic theory for model selection. It also discusses some selected applications to variable selection, change points detection and statistical learning.

Concentration Inequalities and Model Selection

Concentration Inequalities and Model Selection
Author: Pascal Massart
Publisher: Springer
Total Pages: 337
Release: 2007
Genre: Combinatorial probabilities
ISBN: 9786610853335

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Since the impressive works of Talagrand, concentration inequalities have been recognized as fundamental tools in several domains such as geometry of Banach spaces or random combinatorics. They also turn out to be essential tools to develop a non-asymptotic theory in statistics, exactly as the central limit theorem and large deviations are known to play a central part in the asymptotic theory. An overview of a non-asymptotic theory for model selection is given here and some selected applications to variable selection, change points detection and statistical learning are discussed. This volume reflects the content of the course given by P. Massart in St. Flour in 2003. It is mostly self-contained and accessible to graduate students.

Concentration Inequalities

Concentration Inequalities
Author: Stéphane Boucheron
Publisher: Oxford University Press
Total Pages: 492
Release: 2013-02-07
Genre: Mathematics
ISBN: 0199535256

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Describes the interplay between the probabilistic structure (independence) and a variety of tools ranging from functional inequalities to transportation arguments to information theory. Applications to the study of empirical processes, random projections, random matrix theory, and threshold phenomena are also presented.

Stochastic Inequalities and Applications

Stochastic Inequalities and Applications
Author: Evariste Giné
Publisher: Birkhäuser
Total Pages: 362
Release: 2012-12-06
Genre: Mathematics
ISBN: 3034880693

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Concentration inequalities, which express the fact that certain complicated random variables are almost constant, have proven of utmost importance in many areas of probability and statistics. This volume contains refined versions of these inequalities, and their relationship to many applications particularly in stochastic analysis. The broad range and the high quality of the contributions make this book highly attractive for graduates, postgraduates and researchers in the above areas.

An Introduction to Matrix Concentration Inequalities

An Introduction to Matrix Concentration Inequalities
Author: Joel Tropp
Publisher:
Total Pages: 256
Release: 2015-05-27
Genre: Computers
ISBN: 9781601988386

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Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. It is therefore desirable to have tools for studying random matrices that are flexible, easy to use, and powerful. Over the last fifteen years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that achieve all of these goals. This monograph offers an invitation to the field of matrix concentration inequalities. It begins with some history of random matrix theory; it describes a flexible model for random matrices that is suitable for many problems; and it discusses the most important matrix concentration results. To demonstrate the value of these techniques, the presentation includes examples drawn from statistics, machine learning, optimization, combinatorics, algorithms, scientific computing, and beyond.

Universal Coding and Order Identification by Model Selection Methods

Universal Coding and Order Identification by Model Selection Methods
Author: Élisabeth Gassiat
Publisher: Springer
Total Pages: 158
Release: 2018-07-28
Genre: Computers
ISBN: 3319962620

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The purpose of these notes is to highlight the far-reaching connections between Information Theory and Statistics. Universal coding and adaptive compression are indeed closely related to statistical inference concerning processes and using maximum likelihood or Bayesian methods. The book is divided into four chapters, the first of which introduces readers to lossless coding, provides an intrinsic lower bound on the codeword length in terms of Shannon’s entropy, and presents some coding methods that can achieve this lower bound, provided the source distribution is known. In turn, Chapter 2 addresses universal coding on finite alphabets, and seeks to find coding procedures that can achieve the optimal compression rate, regardless of the source distribution. It also quantifies the speed of convergence of the compression rate to the source entropy rate. These powerful results do not extend to infinite alphabets. In Chapter 3, it is shown that there are no universal codes over the class of stationary ergodic sources over a countable alphabet. This negative result prompts at least two different approaches: the introduction of smaller sub-classes of sources known as envelope classes, over which adaptive coding may be feasible, and the redefinition of the performance criterion by focusing on compressing the message pattern. Finally, Chapter 4 deals with the question of order identification in statistics. This question belongs to the class of model selection problems and arises in various practical situations in which the goal is to identify an integer characterizing the model: the length of dependency for a Markov chain, number of hidden states for a hidden Markov chain, and number of populations for a population mixture. The coding ideas and techniques developed in previous chapters allow us to obtain new results in this area. This book is accessible to anyone with a graduate level in Mathematics, and will appeal to information theoreticians and mathematical statisticians alike. Except for Chapter 4, all proofs are detailed and all tools needed to understand the text are reviewed.

High-Dimensional Probability

High-Dimensional Probability
Author: Roman Vershynin
Publisher: Cambridge University Press
Total Pages: 299
Release: 2018-09-27
Genre: Business & Economics
ISBN: 1108415199

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An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Concentration of Measure Inequalities in Information Theory, Communications, and Coding

Concentration of Measure Inequalities in Information Theory, Communications, and Coding
Author: Maxim Raginsky
Publisher:
Total Pages: 256
Release: 2014
Genre: Computers
ISBN: 9781601989062

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Concentration of Measure Inequalities in Information Theory, Communications, and Coding focuses on some of the key modern mathematical tools that are used for the derivation of concentration inequalities, on their links to information theory, and on their various applications to communications and coding.

Concentration of Measure for the Analysis of Randomized Algorithms

Concentration of Measure for the Analysis of Randomized Algorithms
Author: Devdatt P. Dubhashi
Publisher: Cambridge University Press
Total Pages: 213
Release: 2009-06-15
Genre: Computers
ISBN: 1139480995

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Randomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the Chernoff–Hoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as Chernoff–Hoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.

Learning Theory

Learning Theory
Author: John Shawe-Taylor
Publisher: Springer Science & Business Media
Total Pages: 657
Release: 2004-06-17
Genre: Computers
ISBN: 3540222820

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This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004. The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.