Overview

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.

Full Product Details

9780470081471

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Reviews

The authors' efforts to tailor the book to suit the needs of engineering students should pay off in the long run, as they have made the book more relevant and lively. The choice of topics covered is excellent. The rich content and information in this book should make this book a handy reference for many applied research workers. (Technometrics, May 2008) ...an excellent introductory text to modern nonparametric methodology and also should make a useful reference for engineers and statisticians. The mixture of exemplary scholarship, good exposition, insightful examples, and occasional dashes of humor make this book an enjoyable read. (Journal of the American Statistical Association Sept 2008). This book is clearly written and well organized. I liked very much the photos and historical details of statisticians. (International Statistical Review, 2008)

The authors' efforts to tailor the book to suit the needs of engineering students should pay off in the long run, as they have made the book more relevant and lively. The choice of topics covered is excellent. The rich content and information in this book should make this book a handy reference for many applied research workers. (Technometrics, May 2008) The authors' efforts to tailor the book to suit the needs of engineering students should pay off in the long run, as they have made the book more relevant and lively. The choice of topics covered is excellent. The rich content and information in this book should make this book a handy reference for many applied research workers. (Technometrics, May 2008) ?an excellent introductory text to modern nonparametric methodology and also should make a useful reference for engineers and statisticians. The mixture of exemplary scholarship, good exposition, insightful examples, and occasional dashes of humor make this book an enjoyable read. (Journal of the American Statistical Association, September 2008) The book is an essential textbook for graduate courses in engineering and the physical sciences, and is also a valuable reference work for practitioners. It is accessible and thus useful to a wide audience. (Computing Reviews, Feb 2008) This book is clearly written and well organized. I liked very much the photos and historical details of statisticians. (International Statistical Review, 2008)

Author Information

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.

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