Nonparametric statistics with applications to science and engineering with R / Paul Kvam, Brani Vidakovic and Seong-Joon Kim.

Format:

Book

Author/Creator:

Kvam, Paul H., 1962- author.

Vidakovic, Brani, author.

Kim, Seong-Joon, 1984- author.

Series:

Wiley series in probability and statistics.

Wiley series in probability and statistics

Language:

English

Subjects (All):

Nonparametric statistics.

Science--Statistical methods.

Science.

Engineering--Statistical methods.

Engineering.

Physical Description:

1 online resource (451 pages)

Edition:

Second edition.

Place of Publication:

Wiley 2022

Summary:

"This book presents modern nonparametric statistics from a practical point of view. This new edition includes custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in 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. Following an introduction and a discussion of the basics of probability, statistics, and Bayesian statistics, the book discusses order statistics, Kolmogorov-Smirnov test statistic, rank tests, and designed experiments. Next, categorical data, estimating distribution functions, and density estimation is examined. Least squares regression is covered, along with curve fitting techniques, wavelets, and bootstrap sampling. Other topics examined include EM algorithm, statistical learning, nonparametric Bayes, and WinBUGS. This book will be of interest to 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"-- Provided by publisher.

Contents:

Chapter 1 Introduction Chapter 2 Probability Basics Chapter 3 Statistics Basics Chapter 4 Bayesian Statistics Chapter 5 Order Statistics Chapter 6 Goodness of Fit Chapter 7 Rank Tests Chapter 8 Designed Experiments Chapter 9 Categorical Data Chapter 10 Estimating Distribution Functions Chapter 11 Density Estimation Chapter 12 Beyond Linear Regression Chapter 13 Curve Fitting Techniques Chapter 14 Wavelets Chapter 15 Bootstrap Chapter 16 EM Algorithm Chapter 17 Statistical Learning Chapter 18 Nonparametric Bayes R Index Author Index Subject Index EULA

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119268154

111926815X

9781119268178

1119268176

OCLC:

1331413393