An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well. These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression.

Attention is paid to the intellectual development of the field, with a thorough review of bibliographical references. Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included.


  • Rank-based techniques including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests are presented
  • Tests are inverted to produce estimates and confidence intervals
  • Multivariate tests are explored
  • Techniques reflecting the dependence of a response variable on explanatory variables are presented
  • Density estimation is explored
  • The bootstrap and jackknife are discussed

This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology, elementary probability, and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.

chapter 1|13 pages


chapter 2|23 pages

One-Sample Nonparametric Inference

chapter 3|29 pages

Two-Sample Testing

chapter 4|25 pages

Methods for Three or More Groups

chapter 5|17 pages

Group Differences with Blocking

chapter 6|15 pages

Bivariate Methods

chapter 7|14 pages

Multivariate Analysis

chapter 8|6 pages

Density Estimation

chapter 9|17 pages

Regression Function Estimates

chapter 10|19 pages

Resampling Techniques