Methodology in robust and nonparametric statistics
 Responsibility
 Jana Jurečková, Pranab Kumar Sen, Jan Picek.
 Language
 English.
 Imprint
 Boca Raton, FL : CRC Press, c2013.
 Physical description
 xv, 394 p. : ill. ; 24 cm.
Access
Available online
 marc.crcnetbase.com CRCnetBASE
Math & Statistics Library
Stacks
Call number  Status 

QA276 .J8675 2013  Unknown 
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Creators/Contributors
 Author/Creator
 Jurečková, Jana, 1940
 Contributor
 Sen, Pranab Kumar, 1937
 Picek, Jan, 1965
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 357384) and indexes.
 Contents

 Introduction and Synopsis Introduction Synopsis Preliminaries Introduction Inference in Linear Models Robustness Concepts Robust and Minimax Estimation of Location Clippings from Probability and Asymptotic Theory Problems Robust Estimation of Location and Regression Introduction MEstimators LEstimators REstimators Minimum Distance and Pitman Estimators Differentiable Statistical Functions Problems Asymptotic Representations for LEstimators Introduction Bahadur Representations for Sample Quantiles LStatistics with Smooth Scores General LEstimators Statistical Functionals SecondOrder Asymptotic Distributional Representations LEstimation in Linear Model Breakdown Point of L and MEstimators Further Developments Problems Asymptotic Representations for MEstimators Introduction MEstimation of General Parameters MEstimation of Location: Fixed Scale Studentized MEstimators of Location MEstimation in Linear Model Studentizing Scale Statistics Hadamard Differentiability in Linear Models Further Developments Problems Asymptotic Representations for REstimators Introduction Asymptotic Representations for REstimators of Location Representations for REstimators in Linear Model Regression Rank Scores Inference Based on Regression Rank Scores Bibliographical Notes Problems Asymptotic Interrelations of Estimators Introduction Estimators of location Estimation in linear model Approximation by OneStep Versions Further developments Problems Robust Estimation: Multivariate Perspectives Introduction The Notion of Multivariate Symmetry Multivariate Location Estimation Multivariate Regression Estimation AffineEquivariant Robust Estimation Efficiency and Minimum Risk Estimation SteinRule Estimators and Minimum Risk Efficiency Robust Estimation of Multivariate Scatter Some Complementary and Supplementary Notes Problems Robust Tests and Confidence Sets Introduction MTests and RTests Minimax Tests Robust Confidence Sets Multiparameter Confidence Sets AffineEquivariant Tests and Confidence Sets Problems Robust Estimation: Multivariate Perspectives Introduction The Notion of Multivariate Symmetry Multivariate Location Estimation Multivariate Regression Estimation AffineEquivariant Robust Estimation Efficiency and Minimum Risk Estimation SteinRule Estimators and Minimum Risk Efficiency Robust Estimation of Multivariate Scatter Some Complementary and Supplementary Notes Problems Robust Tests and Confidence Sets Introduction MTests and RTests Minimax Tests Robust Confidence Sets Multiparameter Confidence Sets AffineEquivariant Tests and Confidence Sets Problems.
 (source: Nielsen Book Data)
 Publisher's Summary
 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 highlevel computerbased 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 uptodate, this book * Presents multivariate robust and nonparametric estimation with special emphasis on affineequivariant procedures, followed by hypotheses testing and confidence sets * Keeps mathematical abstractions at bay while remaining largely theoretical * Provides a pool of basic mathematical tools used throughout the book in derivations of main results 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.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2013
 ISBN
 9781439840689 (hbk.)
 1439840687 (hbk.)