178,10 €
197,89 €
-10% with code: EXTRA
Methodology in Robust and Nonparametric Statistics
Methodology in Robust and Nonparametric Statistics
178,10
197,89 €
  • We will send in 10–14 business days.
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 underpinning…
  • Publisher:
  • ISBN-10: 0367381060
  • ISBN-13: 9780367381066
  • Format: 15.5 x 23.1 x 2.5 cm, softcover
  • Language: English
  • SAVE -10% with code: EXTRA

Methodology in Robust and Nonparametric Statistics (e-book) (used book) | bookbook.eu

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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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  • Author: Jana Jurečková
  • Publisher:
  • ISBN-10: 0367381060
  • ISBN-13: 9780367381066
  • Format: 15.5 x 23.1 x 2.5 cm, softcover
  • Language: English English

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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