Nonparametric Hypothesis Testing / Wiley Series in Probability and Statistics (ePub)
Rank and Permutation Methods with Applications in R
(Sprache: Englisch)
A novel presentation of rank and permutation tests, with accessible guidance to applications in R
Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book...
Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book...
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A novel presentation of rank and permutation tests, with accessible guidance to applications in R
Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size.
Key Features:
* Examines the most widely used methodologies of nonparametric testing.
* Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies.
* Presents and discusses solutions to the most important and frequently encountered real problems in different fields.
Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes.
Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.
Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size.
Key Features:
* Examines the most widely used methodologies of nonparametric testing.
* Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies.
* Presents and discusses solutions to the most important and frequently encountered real problems in different fields.
Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes.
Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.
Autoren-Porträt von Stefano Bonnini, Livio Corain, Marco Marozzi, Luigi Salmaso
Stefano Bonnini, Assistant Professor of Statistics, Faculty of Economics, Department of Economics, University of Ferrara, Italy.Livio Corain, Assistant Professor of Statistics, Faculty of Engineering, Department of Management and Engineering, University of Padova, Italy.
Marco Marozzi, Associate Professor of Statistics, Faculty of Economics, Department of Economics and Statistics, University of Calabria, Italy.
Luigi Salmaso, Full Professor of Statistics, Faculty of Engineering, University of Padova, Italy.
Bibliographische Angaben
- Autoren: Stefano Bonnini , Livio Corain , Marco Marozzi , Luigi Salmaso
- 2014, 1. Auflage, 256 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1118763483
- ISBN-13: 9781118763483
- Erscheinungsdatum: 01.07.2014
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 2.23 MB
- Mit Kopierschutz
Sprache:
Englisch
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