Nonparametric Statistical Methods Using R (Chapman & Hall/CRC Texts in Statistical Science)

Kloke, John

ISBN 10: 1439873437 / ISBN 13: 9781439873434

Published by Chapman and Hall/CRC, 2014

About this Item

Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. Seller Inventory # Z1-V-019-01658

Bibliographic Details

Title: Nonparametric Statistical Methods Using R (...

Publisher: Chapman and Hall/CRC

Publication Date: 2014

Binding: Hardcover

Condition: Good

About this title

Synopsis:

A Practical Guide to Implementing Nonparametric and Rank-Based Procedures

Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.

The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data.

The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.

Review:

"In general, this textbook is a good addition to the sparse offerings in entry-level nonparametrics. This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R. As R becomes more ubiquitous and data science grows into its own, I think this approach will become more common and this book will be shown to be ahead of its time." (The American Statistician)