Introduction to Nonparametric Statistics

Nonparametric Statistics is a statistical analysis technique that does not make strong assumptions about the distributions generating the data. It covers rank-based and resampling techniques, one-sample testing and estimation, multi-sample testing and estimation, and regression. Computational tools in R and SAS are developed and illustrated via examples. The text is intended for a graduate student in applied statistics and should be taken after an introductory course in statistical methodology, elementary probability, and regression.

Format: Hardback
Length: 212 pages
Publication date: 29 September 2020
Publisher: Taylor & Francis Ltd

An Introduction to Nonparametric Statistics provides comprehensive techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. It encompasses a wide range of methods, including rank-based and resampling techniques, while also considering robust techniques. These techniques encompass one-sample testing and estimation, multi-sample testing and estimation, and regression.

The text emphasizes the intellectual development of the field, offering a thorough review of bibliographical references. Computational tools, developed in R and SAS, are illustrated through examples, with accompanying exercises designed to reinforce the concepts.

The book covers various features, such as rank-based techniques such as sign, Kruskal-Wallis, Friedman, Mann-Whitney, and Wilcoxon tests. It also explores tests that are inverted to produce estimates and confidence intervals, multivariate tests, techniques that reflect the dependence of a response variable on explanatory variables, density estimation, and the bootstrap and jackknife methods.

This text is specifically designed for graduate students in applied statistics. It is recommended to take this course after completing an introductory course in statistical methodology, elementary probability, and regression. Mathematical prerequisites include calculus, including multivariate differentiation and integration, and ideally, a course in matrix algebra. By mastering these techniques, students will gain a solid foundation in nonparametric statistics and be well-equipped to apply them in various research and data analysis scenarios.

Weight: 480g
Dimension: 162 x 241 x 20 (mm)
ISBN-13: 9780367194840