Nonparametric Statistics with Applications to Science and Engineering with R
Language: English
Published by John Wiley and Sons Inc, US, 2022
Series: Book 337 of 358 - Wiley Series in Probability and Statistics
- Hardcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction 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 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. 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 Chi-square and goodness-of-fit, 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. …
Seller Inventory # LU-9781119268130
- Title
- Nonparametric Statistics with Applications to Science and Engineering with R
- Author
- Brani Vidakovic, Seong-joon Kim, Paul Kvam
- Publisher
- John Wiley and Sons Inc, US
- Publication year
- 2022
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 1119268133
- ISBN 13
- 9781119268130
- Edition
- 2nd Edition
- Item weight
- 862 grams
- Dimensions
- 16.51 x 2.54 x 24.13 cm
- Series
- Book 337 of 358: Wiley Series in Probability and Statistics
Introduction 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 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.
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
- Chi-square and goodness-of-fit, 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.
"Synopsis" may belong to another edition of this title.
About the Author
Paul Kvam is professor in the Department of Mathematics, University of Richmond, USA. He received his Ph.D. from University of California, Davis.
Brani Vidakovic is professor in the Department of Statistics, Texas A&M University, USA. He received his Ph.D from Purdue University.
Seong-joon Kim is assistant professor in Department of Industrial Engineering, Chosun University, South Korea. He received his Ph.D. from Hanyang University.
"About the title" may belong to another edition of this title.
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