(Chapman & Hall/CRC Texts in Statistical Science) 1st Edition
by John E. Kolassa (Author)
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.
