Nonparametric Statistics with Applications to Science and Engineering (Wiley Series in Probability and Statistics) | 
 enlarge | Authors: Paul H. Kvam, Brani Vidakovic
Publisher: Wiley-Interscience
Category: Book
List Price: $115.00
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Rating:  1 reviews
Sales Rank: 892924
Media: Hardcover
Edition: 1
Pages: 420
Number Of Items: 1
Shipping Weight (lbs): 1.7
Dimensions (in): 9.4 x 6.4 x 1.1
ISBN: 0470081473
Dewey Decimal Number: 519.5
EAN: 9780470081471
Publication Date: July 23, 2007
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Product Description
This book presents modern nonparametric statistics from a practical point of view. It is primarily intended for use with engineers and scientists. While the book covers the necessary theorems and methods of rank tests in an applied fashion, the novelty lies in its emphasis on modern nonparametric methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical and nonparametric likelihood, and goodness of fit testing. MATLAB is the computing and programming system of choice throughout the book because of its special applicability for research analysis and simulation.
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| Customer Reviews:
modern nonparametrics with engineering applications January 24, 2008
Michael R. Chernick (Holland PA)
25 out of 25 found this review helpful
The authors are both professor at the Georgia Institute of Technology and are accomplished statistical researchers. Vidakovic is an expert probabilist as well, and has also written a probability text on wavelets. He teaches biomedical engineering. Both authors have taught a graduate level engineering course in nonparametric statistics and they both have done some research in nonparametric methods. This text is very modern as it includes bootstrap methods, Bayesian nonparametric methods and wavelets with an eye toward engineering applications. The first five chapters are simply a review of basic concepts in probability and statistics, then in Chapter 6 goodness of fit methods are covered. Chapters 6 - 10 cover the standard topics. Interesting features are the introduction of pictures of famous statisticians who have contributed methods with their names associated with them. Mann and Whitney, Kruskal and Wallace, Fisher and Friedman are among the ones that belong in this group.
Chapter 11 covers density estimation and Chapter 12 covers robust regression, isotonic regression and generalized linear models. The remaining chapters cover curve fitting, wavelets and the bootstrap with the engineering models and applications and are very valuable modern techniques with engineering applications. Statistical learning methods are introduced and sometimes are important to engineers and engineering applications. This is an excellent text and could be a useful reference for engineers and statisticians.
I will be reviewing this book in the future for JASA and will be more detailed in my coverage there.
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