Nonparametric Statistical Methods Using R: A Comprehensive Guide

Discover the thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R, a valuable resource for anyone looking to apply nonparametric techniques in R.

Praise for the First Edition

“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.” – The American Statistician

What’s New in the Second Edition

This latest edition covers traditional nonparametric methods and rank-based analyses, as well as two new chapters on multivariate analyses and big data. The core classical nonparametrics chapters have been expanded to include discussions on ties as well as power and sample size determination.

Key Features and Topics Covered

• Wide range of models: location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type
• Robust methods: linear model analyses, big data, time-to-event analyses, timeseries, and multivariate
• Machine learning topics: k-nearest neighbors and trees
• Practical applications: numerous examples illustrate the methods and their computation
• R packages and datasets: available for computation and exploration

Who is This Book For?

This comprehensive guide is suitable for:

• Advanced undergraduate and graduate students in statistics and data science
• Students of other majors with a solid background in statistical methods, including regression and ANOVA
• Researchers working with nonparametric and rank-based methods in practice

Book Details

Published by CRC Press | Language: English | ISBN: 9780367651350, 9781040025154 | Release Date: May 20, 2024