An Introduction to Nonparametric Statistics PDF

by John E. Kolassa

Part of the Chapman & Hall/CRC Texts in Statistical Science series

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Description

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data.ã€€Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well.ã€€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.ã€€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.ã€€The course is best taken after an introductory course in statistical methodology, elementary probability, and regression.ã€€Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.