Название: Nonparametric Statistical Methods Using R, 2nd Edition
Автор: John Kloke, Joseph McKean
Издательство: CRC Press
Год: 2024
Страниц: 466
Язык: английский
Формат: pdf (true)
Размер: 32.6 MB

This thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R 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.

We have provided numerous examples of real and simulated datasets that illustrate the methods and their computation in R. Hence, we feel that this book also serves as an informative handbook for the researcher wishing to implement nonparametric and rank-based methods in practice. Given that R has continued to grow in popularity, we think that many readers will have already taken a course in R or an applied course that used R extensively. For the vast majority of the book, we have used core R (or base R) rather than add-on packages such as Tidyverse. Some will likely consider this omission a mistake, as many users and institutions have fully adopted the paradigm. The two authors, however, have found that the graphics and syntax of base R are suitable for many of the problems found in research and practice that we have encountered.

The popularity of R continues to grow, and we think it is likely many readers have experience working with R. In particular, the following are assumed:

• Readers are familiar with reading external data and package management as well as the Comprehensive R Archive Network (CRAN).
• Readers have knowledge of common object types including lists, matrices, data frames, along with their helper functions, e.g., length, dim, [].
• Readers have experience with R statistical functionality such as mean, sd as well as important base R functions such as c, rep, seq, sample.

Key Features:

Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.
Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.
Numerous examples illustrate the methods and their computation.
R packages are available for computation and datasets.
Contains two completely new chapters on big data and multivariate analysis.

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.

“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