Nonparametric Statistical Methods Using R

<p><b>Praise for the first edition:</b></p><p>“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.”<br/>-The American Statistician</p><p>This thoroughly updated and expanded second edition of <b>Nonparametric Statistical Methods Using R</b> covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination.  Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition.</p><p>Key Features:</p><ul><li>Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.</li><li>Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.</li><li>Numerous examples illustrate the methods and their computation.</li><li>R packages are available for computation and datasets.</li><li>Contains two completely new chapters on big data and multivariate analysis.</li></ul><p>The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.</p>