Introduction to robust estimation and hypothesis testing / Rand R. Wilcox

Von:

Wilcox, Rand R [aut]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Statistical modeling and decision scienceVerlag: Amsterdam ; Boston : Academic Press, 2012Auflage: 3rd ed (Online-Ausg.)Beschreibung: Online-Ressource (1 online resource (xxi, 690 p.)) : illISBN:

9781283394086
9780123870155

Schlagwörter:

Andere physische Formen: 9780123869838 | 9780123870155 | Druckausg.: Introduction to robust estimation and hypothesis testing. 3rd ed. Amsterdam : Elsevier/Academic Press, 2012. XIX, 690 S.MSC: MSC: *62F10 | 62F03 | 62F35 | 62-01RVK: RVK: SK 830 | QH 233 | CM 4000LOC-Klassifikation:

QA276.8

Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: 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 Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables. Covers latest developments in robust regression Covers latest improvements in ANOVA Includes newest rank-based methods Describes and illustrated easy to use software.Zusammenfassung: 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 -- 2.2 Some Measures of Location and Their Influence Function -- 2.2.1 Quantiles -- 2.2.2 The Winsorized Mean -- 2.2.3 The Trimmed Mean -- 2.2.4 M-Measures of Location -- 2.2.5 R-Measures of Location -- 2.3 Measures of Scale -- 2.4 Scale Equivariant M-Measures of Location -- 2.5 Winsorized Expected Values -- 3 Estimating Measures of Location and Scale -- 3.1 A Bootstrap Estimate of a Standard Error -- 3.1.1 R Function bootse -- 3.2 Density Estimators -- 3.2.1 Normal Kernel -- 3.2.2 Rosenblatt's Shifted Histogram -- 3.2.3 The Expected Frequency Curve -- 3.2.4 An Adaptive Kernel Estimator -- 3.2.5 R Functions skerd, kerden, kdplot, rdplot, akerd, and splot -- 3.3 The Sample Trimmed Mean -- 3.3.1 R Functions mean, tmean, and lloc -- 3.3.2 Estimating the Standard Error of the Trimmed Mean -- 3.3.3 Estimating the Standard Error of the Sample Winsorized Mean -- 3.3.4 R Functions winmean, winvar, trimse, and winse -- 3.3.5 Estimating the Standard Error of the Sample Median, M -- 3.3.6 R Function msmedse -- 3.4 The Finite Sample Breakdown Point -- 3.5 Estimating Quantiles -- 3.5.1 Estimating the Standard Error of the Sample Quantile -- 3.5.2 R Function qse -- 3.5.3 The Maritz-Jarrett Estimate of the Standard Error of x?q -- 3.5.4 R Function mjse.PPN: PPN: 165239706XPackage identifier: Produktsigel: ZDB-26-MYL | BSZ-30-PQE-K1DLR | ZDB-30-PAD | ZDB-30-PQE

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