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Nonparametric Statistics With Applications To Science And Engineering 1st Edition Kvam

  • SKU: BELL-55229950
Nonparametric Statistics With Applications To Science And Engineering 1st Edition Kvam
$ 31.00 $ 45.00 (-31%)

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Nonparametric Statistics With Applications To Science And Engineering 1st Edition Kvam instant download after payment.

Publisher: Wiley-Interscience
File Extension: PDF
File size: 18.38 MB
Pages: 431
Author: Kvam, Paul H., Vidakovic, Brani
ISBN: 9780470081471, 0470081473
Language: English
Year: 2007
Edition: 1

Product desciption

Nonparametric Statistics With Applications To Science And Engineering 1st Edition Kvam by Kvam, Paul H., Vidakovic, Brani 9780470081471, 0470081473 instant download after payment.

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.

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