An Introduction to Nonparametric Statistics

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.

Features

• 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.