Nonparametric statistics with applications to science and engineering with R
Description
Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code
Nonparametric Statistics with Applications to Science and Engineering 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 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.
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, with answers available on a companion site for students and instructors.
Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering 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
- Chi-square and goodness-of-fit, contingency tables, fisher exact test, MC Nemar test, Cochran’s test, Mantel-Haenszel test, and Simpson’s paradox
Nonparametric Statistics with Applications to Science and Engineering 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.
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Citations
Kvam, P. H., Vidakovic, B., & Kim, S. (2022). Nonparametric statistics with applications to science and engineering with R (Second edition.). Wiley.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Kvam, Paul H., 1962-, Brani Vidakovic and Seong-Joon Kim. 2022. Nonparametric Statistics With Applications to Science and Engineering With R. Hoboken, NJ, USA: Wiley.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Kvam, Paul H., 1962-, Brani Vidakovic and Seong-Joon Kim. Nonparametric Statistics With Applications to Science and Engineering With R Hoboken, NJ, USA: Wiley, 2022.
Harvard Citation (style guide)Kvam, P. H., Vidakovic, B. and Kim, S. (2022). Nonparametric statistics with applications to science and engineering with R. Second edn. Hoboken, NJ, USA: Wiley.
MLA Citation, 9th Edition (style guide)Kvam, Paul H., Brani Vidakovic, and Seong-Joon Kim. Nonparametric Statistics With Applications to Science and Engineering With R Second edition., Wiley, 2022.
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Grouping Information
| Grouped Work ID | 82903e10-fc1e-d2e5-a3ca-1f63c4ce61e9-eng |
|---|---|
| Full title | nonparametric statistics with applications to science and engineering with r |
| Author | kvam paul h |
| Grouping Category | book |
| Last Update | 2025-01-24 12:33:29PM |
| Last Indexed | 2025-03-27 03:20:12AM |
Book Cover Information
| Image Source | syndetics |
|---|---|
| First Loaded | Aug 17, 2023 |
| Last Used | Apr 5, 2025 |
Marc Record
| First Detected | Mar 20, 2023 10:19:19 AM |
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| Last File Modification Time | Dec 17, 2024 08:21:31 AM |
| Suppressed | Record had no items |
MARC Record
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| 100 | 1 | |a Kvam, Paul H.,|d 1962-|e author. | |
| 245 | 1 | 0 | |a Nonparametric statistics with applications to science and engineering with R /|c 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. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Hoboken, NJ, USA :|b Wiley,|c [2022] | |
| 300 | |a 1 online resource. | ||
| 336 | |a text|b txt|2 rdacontent | ||
| 337 | |a computer|b c|2 rdamedia | ||
| 338 | |a online resource|b cr|2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and statistics | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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"--|c Provided by publisher. | ||
| 588 | |a Description based on print version record and CIP data provided by publisher; resource not viewed. | ||
| 590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Nonparametric statistics.|9 49115 | |
| 650 | 0 | |a Science|x Statistical methods.|9 99899 | |
| 650 | 0 | |a Engineering|x Statistical methods.|9 37279 | |
| 700 | 1 | |a Vidakovic, Brani,|d 1955-|e author. | |
| 700 | 1 | |a Kim, Seong-Joon,|d 1984-|e author. | |
| 776 | 0 | 8 | |i Print version:|a Kvam, Paul H., 1962-|t Nonparametric statistics with applications to science and engineering with R|b Second edition.|d Hoboken, NJ, USA : Wiley, [2022]|z 9781119268130|w (DLC) 2022013281 |
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