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Praise for the first edition:
"This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R."
-The American Statistician
This thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition.
Key Features:
- Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.
- Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.
- Numerous examples illustrate the methods and their computation.
- R packages are available for computation and datasets.
- Contains two completely new chapters on big data and multivariate analysis.
The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.
Inhaltsverzeichnis
1. Introduction 2. One-Sample Problems 3. Two-Sample Problems 4. Regression 5. ANOVA-Type Rank-Based Procedures 6. Categorical 7. Linear Models 8. Topics in Regression 9. Cluster Correlated Data 10. Multivariate Analysis 11. Big Data Appendix - R Version Information
Über den Autor / die Autorin
John D. Kloke is a bit of a jack-of-all-trades as he has worked as a clinical trial statistician supporting industry as well as academic studies and he also served as a teacher-scholar at several academic institutions. He has held faculty positions at the University of California - Santa Barbara, University of Wisconsin - Madison, University of Pittsburgh, Bucknell University, and Pomona College. An early adopter of R, he is an author and maintainer of numerous R packages, including Rfit and npsm. He has published papers on nonparametric rank-based estimation, including analysis of cluster correlated data.
Joseph W. McKean is a professor emeritus of statistics at Western Michigan University. He has published many papers on nonparametric and robust statistical procedures and has co-authored several books, including Robust Nonparametric Statistical Methods and Introduction to Mathematical Statistics. He co-edited the book Robust Rank-Based and Nonparametric Methods. He served as an associate editor of several statistics journals and is a fellow of the American Statistical Association.
Zusammenfassung
Praise for the first edition:
“This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.”
-The American Statistician
This thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition.
Key Features:
- Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.
- Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.
- Numerous examples illustrate the methods and their computation.
- R packages are available for computation and datasets.
- Contains two completely new chapters on big data and multivariate analysis.
The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.
