Nonparametric Statistics with Applications to Science and Engineering 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Nonparametric Statistics with Applications to Science and Engineering

简体网页||繁体网页
Vidakovic, Brani/ Kvam, Paul H. 作者
John Wiley & Sons Inc
译者
2007-7 出版日期
420 页数
1058.00 元 价格
HRD
丛书系列
9780470081471 图书编码

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发表于2024-12-24


Nonparametric Statistics with Applications to Science and Engineering 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Nonparametric Statistics with Applications to Science and Engineering 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Nonparametric Statistics with Applications to Science and Engineering 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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Nonparametric Statistics with Applications to Science and Engineering 在线电子书 图书描述

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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