Robust Statistical Methods with R, Second Edition

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A01=Jan Picek
A01=Jana Jureckova
A01=Martin Schindler
advanced data visualization
Asymptotic Relative Efficiency
Asymptotically Normal Distribution
Author_Jan Picek
Author_Jana Jureckova
Author_Martin Schindler
Breakdown Point
Category=PBT
Classical Statistical Procedures
Distribution Function
Empirical Probability Distribution
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Eventual Measurement Errors
Finite Fisher Information
Finite Sample Breakdown Point
Hadamard Derivative
Hellinger Distance
Linear Rank Statistic
LS Regression
Marginal MLE
measurement error
measurement error correction
Measurement Error Models
Middle Order Statistic
Minimal Asymptotic Variance
Minimum Risk Equivariant Estimator
multivariate analysis techniques
multivariate data
Multivariate Parametric Estimation
Multivariate Quantiles
nonparametric statistical procedures
Positive Definite Dispersion Matrix
Qualitatively Robust
R statistical package
rank-based estimation
Regression Quantile
robust estimation in measurement error models
robust regression methods
robust statistical methods
Robust Statistical Procedures
statistical inference theory
Unknown Distribution Function

Product details

  • ISBN 9781032092607
  • Weight: 390g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Jun 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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

Jana Jurečková is a Professor of Statistics at the Charles University, Prague.

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