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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
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 Presents
multivariate robust and nonparametric estimation with special
emphasis on affine-equivariant 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.
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 Presents
multivariate robust and nonparametric estimation with special
emphasis on affine-equivariant 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.
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
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