Nonparametric Statistics with Applications to Science and Engineering

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.

Spis treści:
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 Ind ex.
Author Index.
Subject Index.

Nota biograficzna:
Paul H. Kvam, PhD, 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, PhD, 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.

Okładka tylna:
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.