Nonparametric Statistical Methods Using R
by John Kloke, Joseph McKean
This thoroughly updated and expanded second edition 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
Hardcover
Brand New
Publisher Description
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 StatisticianThis 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 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.
Table of Contents
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
Review
"In my opinion, the authors of this book have successfully managed to compile a significant portion of the topics addressed in nonparametric statistics courses into a cohesive framework, with the difficulty of the material gradually increasing throughout. The accompanying code has been updated to ensure functionality. [...] I highly recommend this book to readers who are looking for practical insights into nonparametric statistics and prefer an applied approach, while still offering enough depth for those interested in theoretical study."
-Bojana Miloševi in The American Statistician, May 2025
Details
