Nonparametric Statistical Methods Using R: 1st Edition (Hardback) book cover

Nonparametric Statistical Methods Using R

1st Edition

By John Kloke, Joseph W. McKean

Chapman and Hall/CRC

287 pages | 58 B/W Illus.

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pub: 2014-10-09
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A Practical Guide to Implementing Nonparametric and Rank-Based Procedures

Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.

The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data.

The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.


"In general, this textbook is a good addition to the sparse offerings in entry-level nonparametrics. 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. As R becomes more ubiquitous and data science grows into its own, I think this approach will become more common and this book will be shown to be ahead of its time." (The American Statistician)

Table of Contents

Getting Started with R

R Basics

Reading External Data

Generating Random Data


Repeating Tasks

User-Defined Functions

Monte Carlo Simulation

R Packages

Basic Statistics

Sign Test

Signed-Rank Wilcoxon



One- and Two-Sample Proportion Problems

χ2 Tests

Two-Sample Problems

Introductory Example

Rank-Based Analyses

Scale Problem

Placement Test for the Behrens–Fisher Problem

Efficiency and Optimal Scores

Adaptive Rank Scores Tests

Regression I

Simple Linear Regression

Multiple Linear Regression

Linear Models

Aligned Rank Tests


Nonparametric Regression




Multi-Way Crossed Factorial Design


Methodology for Type III Hypotheses Testing

Ordered Alternatives

Multi-Sample Scale Problem

Time-to-Event Analysis

Kaplan–Meier and Log Rank Test

Cox Proportional Hazards Models

Accelerated Failure Time Models

Regression II

Robust Diagnostics

Weighted Regression

Linear Models with Skew Normal Errors

A Hogg-Type Adaptive Procedure


Time Series

Cluster Correlated Data

Friedman’s Test

Joint Rankings Estimator

Robust Variance Component Estimators

Multiple Rankings Estimator

GEE-Type Estimator



Exercises appear at the end of each chapter.

About the Authors

John Kloke is a biostatistician and assistant scientist at the University of Wisconsin–Madison. He has held faculty positions at the University of Pittsburgh, Bucknell University, and Pomona College. An R user for more than 15 years, 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 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 is an associate editor of several statistics journals and a fellow of the American Statistical Association.

About the Series

Chapman & Hall/CRC The R Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General