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.
Table of Contents
One-Sample Nonparametric Inference
Methods for Three or More Groups
Group Differences with Blocking
Regression Function Estimates
John Kolassa is Professor of Statistics and Biostatistics, Rutgers, the State University of New Jersey.