Nonparametric Statistics

3rd ISNPS, Avignon, France, June 2016

Author: Patrice Bertail,Delphine Blanke,Pierre-André Cornillon,Eric Matzner-Løber

Publisher: Springer

ISBN: 3319969412

Category: MATHEMATICS

Page: 390

View: 7068

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This volume presents the latest advances and trends in nonparametric statistics, and gathers selected and peer-reviewed contributions from the 3rd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Avignon, France on June 11-16, 2016. It covers a broad range of nonparametric statistical methods, from density estimation, survey sampling, resampling methods, kernel methods and extreme values, to statistical learning and classification, both in the standard i.i.d. case and for dependent data, including big data. The International Society for Nonparametric Statistics is uniquely global, and its international conferences are intended to foster the exchange of ideas and the latest advances among researchers from around the world, in cooperation with established statistical societies such as the Institute of Mathematical Statistics, the Bernoulli Society and the International Statistical Institute. The 3rd ISNPS conference in Avignon attracted more than 400 researchers from around the globe, and contributed to the further development and dissemination of nonparametric statistics knowledge.
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High-Dimensional Statistics

A Non-Asymptotic Viewpoint

Author: Martin J. Wainwright

Publisher: Cambridge University Press

ISBN: 1108571239

Category: Mathematics

Page: N.A

View: 6726

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Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.
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Statistical Models

Author: A. C. Davison

Publisher: Cambridge University Press

ISBN: 9781139437417

Category: Mathematics

Page: N.A

View: 611

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Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.
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Contributions to Probability and Statistics

Applications and Challenges : Proceedings of the International Statistics Workshop, University of Canberra, 4-5 April 2005

Author: Peter Brown

Publisher: World Scientific Publishing Company Incorporated

ISBN: 9789812703910

Category: Business & Economics

Page: 311

View: 7747

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Contributed by world renowned researchers, the book features a wide range of important topics in modern statistical theory and methodology, economics and finance, ecology, education, health and sports studies, and computer and IT-data mining. It is accessible to students and of interest to experts. Many of the contributions are concerned with theoretical innovations, but all have applications in view, and some contain illustrations of the applied methods or photos of historic mathematicians. A few of the notable contributors are Ejaz Ahmed (Windsor), Joe Gani (ANU), Roger Gay (Monash), Atsuhiro Hayashi (NCUEE, Tokyo), Markus Hegland (ANU), Chris Heyde (ANU/Columbia), Jeff Hunter (Massey), Phil Lewis (Canberra), Heinz Neudecker (Amsterdam), Graham Pollard (Canberra), Simo Puntanen (Tampere), George Styan (McGill), and Goetz Trenkler (Dortmund).
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Information Geometry

Author: Nihat Ay,Jürgen Jost,Hông Vân Lê,Lorenz Schwachhöfer

Publisher: Springer

ISBN: 3319564781

Category: Mathematics

Page: 407

View: 648

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The book provides a comprehensive introduction and a novel mathematical foundation of the field of information geometry with complete proofs and detailed background material on measure theory, Riemannian geometry and Banach space theory. Parametrised measure models are defined as fundamental geometric objects, which can be both finite or infinite dimensional. Based on these models, canonical tensor fields are introduced and further studied, including the Fisher metric and the Amari-Chentsov tensor, and embeddings of statistical manifolds are investigated. This novel foundation then leads to application highlights, such as generalizations and extensions of the classical uniqueness result of Chentsov or the Cramér-Rao inequality. Additionally, several new application fields of information geometry are highlighted, for instance hierarchical and graphical models, complexity theory, population genetics, or Markov Chain Monte Carlo. The book will be of interest to mathematicians who are interested in geometry, information theory, or the foundations of statistics, to statisticians as well as to scientists interested in the mathematical foundations of complex systems.
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