Methodology in Robust and Nonparametric Statistics,Used

Methodology in Robust and Nonparametric Statistics,Used

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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 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 bookPresents multivariate robust and nonparametric estimation with special emphasis on affineequivariant procedures, followed by hypotheses testing and confidence setsKeeps mathematical abstractions at bay while remaining largely theoreticalProvides a pool of basic mathematical tools used throughout the book in derivations of main resultsThe 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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