Rank and pseudo-rank procedures for independent observations in factorial designs. Using R and SAS.

*(English)*Zbl 1455.62001
Springer Series in Statistics. Cham: Springer (ISBN 978-3-030-02912-8/hbk; 978-3-030-02914-2/ebook). xx, 521 p. (2019).

From the cover of the book: “This book explains how to analyze independent data from factorial designs without having to make restrictive assumptions, such as normality of the data, or equal variances. The general approach also allows for ordinal and even dichotomous data. The underlying effect size is the nonparametric relative effect, which has a simple and intuitive probability interpretation. The data analysis is presented as comprehensively as possible, including appropriate descriptive statistics which follow a nonparametric paradigm, as well as corresponding inferential methods using hypothesis tests and confidence intervals based on pseudo-ranks.

Offering clear explanations, an overview of the modern rank- and pseudo-rank-based inference methodology and numerous illustrations with real data examples, as well as the necessary R/SAS code to run the statistical analyses, this book is a valuable resource for statisticians and practitioners alike.”

The book is very large structured in the Preface, 8 chapters (divided in 57 subchapters), Appendix A (Software and program code), Appendix B (Data sets and descriptions), Appendix C (Correction), Acknowledgments, References, Index:

Chapter 1. Types of data and designs – Chapter 2. Distributions and effects – Chapter 3. Two samples – Chapter 4. Several samples – Chapter 5. Two-factor crossed designs – Chapter 6. Designs with three and more factors – Chapter 7. Derivation of main results – Chapter 8. Mathematical techniques.

Most of the chapters finish with a subchapter “Exercises and problems” and some subchapters finish with a summary, thus we have more than 110 problems for solving. The bibliography contains more than 250 references and the index more than 900 items.

The book can be very recommended all readers who are interested in this field.

Offering clear explanations, an overview of the modern rank- and pseudo-rank-based inference methodology and numerous illustrations with real data examples, as well as the necessary R/SAS code to run the statistical analyses, this book is a valuable resource for statisticians and practitioners alike.”

The book is very large structured in the Preface, 8 chapters (divided in 57 subchapters), Appendix A (Software and program code), Appendix B (Data sets and descriptions), Appendix C (Correction), Acknowledgments, References, Index:

Chapter 1. Types of data and designs – Chapter 2. Distributions and effects – Chapter 3. Two samples – Chapter 4. Several samples – Chapter 5. Two-factor crossed designs – Chapter 6. Designs with three and more factors – Chapter 7. Derivation of main results – Chapter 8. Mathematical techniques.

Most of the chapters finish with a subchapter “Exercises and problems” and some subchapters finish with a summary, thus we have more than 110 problems for solving. The bibliography contains more than 250 references and the index more than 900 items.

The book can be very recommended all readers who are interested in this field.

Reviewer: Ludwig Paditz (Dresden)

##### MSC:

62-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics |

62R07 | Statistical aspects of big data and data science |

62G10 | Nonparametric hypothesis testing |

62G15 | Nonparametric tolerance and confidence regions |

62G20 | Asymptotic properties of nonparametric inference |

62K15 | Factorial statistical designs |

62P10 | Applications of statistics to biology and medical sciences; meta analysis |

62P15 | Applications of statistics to psychology |

62-04 | Software, source code, etc. for problems pertaining to statistics |