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Introduction to Robust Estimation and Hypothesis Testing.

Author: Rand R Wilcox
Publisher: Burlington : Elsevier Science, 2012.
Edition/Format:   eBook : Document : English : 3rd edView all editions and formats
Database:WorldCat
Summary:
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Rand R Wilcox
ISBN: 9780123870155 0123870151
OCLC Number: 775872063
Description: 1 online resource (713 pages)
Contents: Front Cover; Introduction to Robust Estimation and Hypothesis Testing; Copyright; Table of Contents; Preface; 1 Introduction; 1.1 Problems with Assuming Normality; 1.2 Transformations; 1.3 The Influence Curve; 1.4 The Central Limit Theorem; 1.5 Is the ANOVA F Robust?; 1.6 Regression; 1.7 More Remarks; 1.8 Using the Computer: R; 1.9 Some Data Management Issues; 1.9.1 Eliminating Missing Values; 2 A Foundation for Robust Methods; 2.1 Basic Tools for Judging Robustness; 2.1.1 Qualitative Robustness; 2.1.2 Infinitesimal Robustness; 2.1.3 Quantitative Robustness.

Abstract:

This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations. Introduction to R.

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