Book description
NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH RIntroduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code
Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible.
Relevant builtin functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system.
The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included.
Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include:
 Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments
 Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling
 EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation
 Chisquare and goodnessoffit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood
Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.
Table of contents
 Cover
 Title Page
 Copyright
 Preface
 Acknowledgments
 1 Introduction

2 Probability Basics
 2.1 Helpful Functions
 2.2 Events, Probabilities, and Random Variables
 2.3 Numerical Characteristics of Random Variables
 2.4 Discrete Distributions
 2.5 Continuous Distributions
 2.6 Mixture Distributions
 2.7 Exponential Family of Distributions
 2.8 Stochastic Inequalities
 2.9 Convergence of Random Variables
 2.10 Exercises
 References
 Notes
 3 Statistics Basics
 4 Bayesian Statistics
 5 Order Statistics
 6 Goodness of Fit
 7 Rank Tests
 8 Designed Experiments
 9 Categorical Data
 10 Estimating Distribution Functions
 11 Density Estimation
 12 Beyond Linear Regression
 13 Curve Fitting Techniques
 14 Wavelets
 15 Bootstrap
 16 EM Algorithm
 17 Statistical Learning
 18 Nonparametric Bayes
 Appendix A: WinBUGS
 Appendix B: R Coding
 R Index
 Author Index
 Subject Index
 End User License Agreement
Product information
 Title: Nonparametric Statistics with Applications to Science and Engineering with R, 2nd Edition
 Author(s):
 Release date: October 2022
 Publisher(s): Wiley
 ISBN: 9781119268130
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