Nonparametric Statistics with Applications to Science and Engineering with R, 2nd Edition

Preface

Danger lies not in what we don't know, but in what we think we know that just ain't so.

Mark Twain (1835–1910)

This textbook is a substantial revision of a previous textbook written in 2007 by Kvam and Vidakovic. The biggest difference in this version is the adoption of the R programming language as a supplementary learning tool for the purpose of teaching concepts, illustrating examples, and completing computational homework assignments. In the original book, the authors relied on Matlab.

There has been plenty of change in the world of nonparametric statistics since we finished the first edition of this book. While the statistics community had already adapted to a modern framework for data analysis that relies increasingly on nonparametric procedures (not to mention Bayesian alternatives to traditional inference), we sense more adapters in engineering, medical research, chemistry, biology, and especially the behavioral sciences with each passing year. However, the field of nonparametric statistics has also receded toward the periphery of the statistics curriculum in the wake of data science, which continues to encroach on graduate curriculums associated with statistics, causing more programs to replace traditional statistics courses with the trendier versions involving data structures.

There are quality monographs/texts dealing with nonparametric statistics, such as the encyclopedic book by Hollander and Wolfe, Nonparametric Statistical Methods, or the excellent ...