Nonparametric statistics with applications to science and engineering with R, Paul Kvam, University of Richmond, Richmond, Virginia, USA, Brani Vidakovic, Texas A&M University, College Station, Texas, USA, Seong-joon Kim, Chosun University, Gwangju, South Korea
Type
Label
Nonparametric statistics with applications to science and engineering with R, Paul Kvam, University of Richmond, Richmond, Virginia, USA, Brani Vidakovic, Texas A&M University, College Station, Texas, USA, Seong-joon Kim, Chosun University, Gwangju, South Korea
Language
eng
Bibliography note
Includes bibliographical references and index
Index
index present
Literary form
non fiction
Main title
Nonparametric statistics with applications to science and engineering with R
Nature of contents
bibliographydictionaries
Oclc number
11331413393
Responsibility statement
Paul Kvam, University of Richmond, Richmond, Virginia, USA, Brani Vidakovic, Texas A&M University, College Station, Texas, USA, Seong-joon Kim, Chosun University, Gwangju, South Korea
Series statement
Wiley series in probability and statistics
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