Nonparametric Statistics with Applications to Science and Engineering
Published on 28. June 2008
448 pages
978-0-470-16869-1 (ISBN)
Description
A thorough and definitive book that fully addresses traditionaland modern-day topics of nonparametric statistics
This book presents a practical approach to nonparametricstatistical analysis and provides comprehensive coverage of bothestablished and newly developed methods. With the use of MATLAB,the authors present information on theorems and rank tests in anapplied fashion, with an emphasis on modern methods in regressionand curve fitting, bootstrap confidence intervals, splines,wavelets, empirical likelihood, and goodness-of-fit testing.
Nonparametric Statistics with Applications to Science andEngineering begins with succinct coverage of basic results fororder statistics, methods of
categorical data analysis, nonparametric regression, and curvefitting methods. The authors then focus on nonparametric proceduresthat are becoming more relevant to engineering researchers andpractitioners. The important fundamental materials needed toeffectively learn and apply the discussed methods are also providedthroughout the book.
Complete with exercise sets, chapter reviews, and a related Website that features downloadable MATLAB applications, this book isan essential textbook for graduate courses in engineering and thephysical sciences and also serves as a valuable reference forresearchers who seek a more comprehensive understanding of modernnonparametric statistical methods.
Reviews / Votes
"The authors' efforts to tailor the book to suit the needs ofengineering students should pay off in the long run, as they havemade the book more relevant and lively. The choice of topicscovered is excellent. The rich content and information in this bookshould make this book a handy reference for many applied researchworkers." (Technometrics, May 2008) "The authors' efforts to tailor the book to suit the needs ofengineering students should pay off in the long run, as they havemade the book more relevant and lively. The choice of topicscovered is excellent. The rich content and information inthis book should make this book a handy reference for many appliedresearch workers." (Technometrics, May 2008) "...an excellent introductory text to modern nonparametricmethodology and also should make a useful reference for engineersand statisticians. The mixture of exemplary scholarship, goodexposition, insightful examples, and occasional dashes of humormake this book an enjoyable read." (Journal of the AmericanStatistical Association, September 2008) "The book is an essential textbook for graduate courses inengineering and the physical sciences, and is also a valuablereference work for practitioners. It is accessible and thus usefulto a wide audience." (Computing Reviews, Feb 2008) "This book is clearly written and well organized. I likedvery much the photos and historical details of statisticians."(International Statistical Review, 2008)More details
Other editions
Additional editions
Paul H. Kvam | Brani Vidakovic
Nonparametric Statistics with Applications to Science and Engineering
Book
08/2007
Wiley
Unfortunately, price unknown
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Persons
Paul H. Kvam, PhD, is Professor of Industrial and SystemsEngineering at Georgia Institute of Technology. His researchinterests include nonparametric estimation, statistical reliabilitywith applications to engineering, and analysis of complex anddependent systems. He has written over fifty refereed articles andwas named a Fellow of the American Statistical Association in 2006.
Brani Vidakovic, PhD, is Professor of Statistics andDirector of the Center for Bioengineering Statistics at The WallaceH. Coulter Department of Biomedical Engineering, Georgia Instituteof Technology. He has authored or co-authored three books and haspublished more than four dozen refereed articles. His areas ofinterest include wavelets, Bayesian inference, biostatistics,statistical methods in environmental research, and statisticaleducation.
Content
Preface.
1. Introduction.
2. Probability Basics.
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.
A. MATLAB.
B. WinBUGS.
MATLAB Index.
Author Index.
Subject Index.
