A thorough and definitive book that fully addresses traditional
and modern-day topics of nonparametric statistics
This book presents a practical approach to nonparametric
statistical analysis and provides comprehensive coverage of both
established and newly developed methods. With the use of MATLAB,
the authors present information on theorems and rank tests in an
applied fashion, with an emphasis on modern methods in regression
and curve fitting, bootstrap confidence intervals, splines,
wavelets, empirical likelihood, and goodness-of-fit testing.
Nonparametric Statistics with Applications to Science and
Engineering begins with succinct coverage of basic results for
order statistics, methods of
categorical data analysis, nonparametric regression, and curve
fitting methods. The authors then focus on nonparametric procedures
that are becoming more relevant to engineering researchers and
practitioners. The important fundamental materials needed to
effectively learn and apply the discussed methods are also provided
throughout the book.
Complete with exercise sets, chapter reviews, and a related Web
site that features downloadable MATLAB applications, this book is
an essential textbook for graduate courses in engineering and the
physical sciences and also serves as a valuable reference for
researchers who seek a more comprehensive understanding of modern
nonparametric statistical methods.
Inhaltsverzeichnis
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. Win BUGS.
MATLAB Index.
Author Index.
Subject Index.
Über den Autor
Paul H. Kvam, Ph D, is Professor of Industrial and Systems
Engineering at Georgia Institute of Technology. His research
interests include nonparametric estimation, statistical reliability
with applications to engineering, and analysis of complex and
dependent systems. He has written over fifty refereed articles and
was named a Fellow of the American Statistical Association in 2006.
Brani Vidakovic, Ph D, is Professor of Statistics and
Director of the Center for Bioengineering Statistics at The Wallace
H. Coulter Department of Biomedical Engineering, Georgia Institute
of Technology. He has authored or co-authored three books and has
published more than four dozen refereed articles. His areas of
interest include wavelets, Bayesian inference, biostatistics,
statistical methods in environmental research, and statistical
education.
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