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Methodology in Robust and Nonparametric Statistics (Paperback)
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Methodology in Robust and Nonparametric Statistics (Paperback)
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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.
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