Methodology in Robust and Nonparametric Statistics

Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from biomedical sciences to the public health disciplines, and, more recently, in genomics, bioinformatics, and financial statistics. These disciplines are presently nourished by data mining and high-level computer-based algorithms, but to work actively with robust and nonparametric procedures, practitioners need to understand their background. Explaining the underpinnings of robust methods and recent theoretical developments, Methodology in Robust and Nonparametric Statistics provides a profound mathematically rigorous explanation of the methodology of robust and nonparametric statistical procedures. Thoroughly up-to-date, this book <ul> <li>Presents multivariate robust and nonparametric estimation with special emphasis on affine-equivariant procedures, followed by hypotheses testing and confidence sets</li> <li>Keeps mathematical abstractions at bay while remaining largely theoretical</li> <li>Provides a pool of basic mathematical tools used throughout the book in derivations of main results</li> </ul> The methodology presented, with due emphasis on asymptotics and interrelations, will pave the way for further developments on robust statistical procedures in more complex models. Using examples to illustrate the methods, the text highlights applications in the fields of biomedical science, bioinformatics, finance, and engineering. In addition, the authors provide exercises in the text.

Science & Math

Jurečková, Jana

Sen, Pranab

Picek, Jan

Mathematics

CRC-Press

While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.

Adaptive Regression

Business & Money

Dodge, Yadolah

Education & Reference

Springer

A broad and unified methodology for robust statistics--with exciting new applications  Robust statistics is one of the fastest growing fields in contemporary statistics. It is also one of the more diverse and sometimes confounding areas, given the many different assessments and interpretations of robustness by theoretical and applied statisticians. This innovative book unifies the many varied, yet related, concepts of robust statistics under a sound theoretical modulation. It seamlessly integrates asymptotics and interrelations, and provides statisticians with an effective system for dealing with the interrelations between the various classes of procedures.  Drawing on the expertise of researchers from around the world, and covering over a decade's worth of developments in the field, Robust Statistical Procedures: Asymptotics and Interrelations:  * Discusses both theory and applications in its two parts, from the fundamentals to robust statistical inference  * Thoroughly explores the interrelations between diverse classes of procedures, unlike any other book  * Compares nonparametric procedures with robust statistics, explaining in detail asymptotic representations for various estimators  * Provides a timesaving list of mathematical tools for the problems under discussion  * Keeps mathematical abstractions to a minimum, in spite of its largely theoretical content  * Includes useful problems and exercises at the end of each chapter  * Offers strategies for more complex models when using robust statistical procedures   Self-contained and rounded in approach, this book is invaluable for both applied statisticians and theoretical researchers; for graduate students in mathematical statistics; and for anyone interested in the influence of this methodology.

Robust Statistical Procedures: Asymptotics and Interrelations

Sen, Pranab Kumar

Wiley-Interscience

Robust Statistical Methods with R, Second Edition

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment, and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures, less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics.\nFeatures\n• Provides a systematic, practical treatment of robust statistical methods\n• Offers a rigorous treatment of the whole range of robust methods, including the sequential versions of estimators, their moment convergence, and compares their asymptotic and finite-sample behavior\n• The extended account of multivariate models includes the admissibility, shrinkage effects and unbiasedness of two-sample tests\n• Illustrates the small sensitivity of the rank procedures in the measurement error model\n• Emphasizes the computational aspects, supplies many examples and illustrations, and provides the own procedures of the authors in the R software on the book’s website

Schindler, Martin

Computers & Technology

Routledge

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment, and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures, less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features • Provides a systematic, practical treatment of robust statistical methods • Offers a rigorous treatment of the whole range of robust methods, including the sequential versions of estimators, their moment convergence, and compares their asymptotic and finite-sample behavior • The extended account of multivariate models includes the admissibility, shrinkage effects and unbiasedness of two-sample tests • Illustrates the small sensitivity of the rank procedures in the measurement error model • Emphasizes the computational aspects, supplies many examples and illustrations, and provides the own procedures of the authors in the R software on the book’s website

Chapman and Hall/CRC

Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from biomedical sciences to the public health disciplines, and, more recently, in genomics, bioinformatics, and financial statistics. These disciplines are presently nourished by data mining and high-level computer-based algorithms, but to work actively with robust and nonparametric procedures, practitioners need to understand their background. Explaining the underpinnings of robust methods and recent theoretical developments, Methodology in Robust and Nonparametric Statistics provides a profound mathematically rigorous explanation of the methodology of robust and nonparametric statistical procedures. Thoroughly up-to-date, this book <ul>
<li>Presents multivariate robust and nonparametric estimation with special emphasis on affine-equivariant procedures, followed by hypotheses testing and confidence sets</li> <li>Keeps mathematical abstractions at bay while remaining largely theoretical </li> <li>Provides a pool of basic mathematical tools used throughout the book in derivations of main results</li> </ul> The methodology presented, with due emphasis on asymptotics and interrelations, will pave the way for further developments on robust statistical procedures in more complex models. Using examples to illustrate the methods, the text highlights applications in the fields of biomedical science, bioinformatics, finance, and engineering. In addition, the authors provide exercises in the text.

Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on practical application.The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner. Highlighting hands-on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample propertie

Robust Statistical Methods with R

See the best price to sell, buy, or rent books by Jurečková, Jana. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Jurečková, Jana

See the best price to sell, buy, or rent books by Jurečková, Jana. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Methodology in Robust and Nonparametric Statistics

Adaptive Regression

Robust Statistical Procedures: Asymptotics and Interrelations

Robust Statistical Methods with R, Second Edition

Robust Statistical Methods with R, Second Edition

Methodology in Robust and Nonparametric Statistics

Adaptive Regression

Robust Statistical Methods with R