1st Edition

An Introduction to Nonparametric Statistics




ISBN 9780367194840
Published September 29, 2020 by Chapman and Hall/CRC
224 Pages 35 B/W Illustrations

USD $105.00

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Book Description

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.

Table of Contents

Background

One-Sample Nonparametric Inference

Two-Sample Testing

Methods for Three or More Groups

Group Differences with Blocking

Bivariate Methods

Multivariate Analysis

Density Estimation

Regression Function Estimates

Resampling Techniques

Appendices

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Author(s)

Biography

John Kolassa is Professor of Statistics and Biostatistics, Rutgers, the State University of New Jersey.