Hettmansperger / McKean Robust Nonparametric Statistical Methods, Second Edition

2. Auflage 2011
ISBN: 978-1-4398-0909-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 554 Seiten

Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It follows the approach of the first edition by developing rank-based methods from the unifying theme of geometry. This edition, however, includes more models and methods and significantly extends the possible analyses based on ranks.
New to the Second Edition

- A new section on rank procedures for nonlinear models

- A new chapter on models with dependent error structure, covering rank methods for mixed models, general estimating equations, and time series

- New material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models

Taking a comprehensive, unified approach to statistical analysis, the book continues to describe one- and two-sample problems, the basic development of rank methods in the linear model, and fixed effects experimental designs. It also explores models with dependent error structure and multivariate models. The authors illustrate the implementation of the methods using many real-world examples and R. More information about the data sets and R packages can be found at www.crcpress.com

Zielgruppe

Researchers and graduate students in statistics.

Autoren/Hrsg.

Hettmansperger, Thomas P.

McKean, Joseph W.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

One-Sample Problems
Introduction

Location Model

Geometry and Inference in the Location Model

Examples

Properties of Norm-Based Inference
Robustness Properties of Norm-Based Inference

Inference and the Wilcoxon Signed-Rank Norm

Inference Based on General Signed-Rank Norms

Ranked Set Sampling

L1 Interpolated Confidence Intervals

Two-Sample Analysis
Two-Sample Problems
Introduction

Geometric Motivation
Examples

Inference Based on the Mann-Whitney-Wilcoxon
General Rank Scores

L1 Analyses

Robustness Properties

Proportional Hazards

Two-Sample Rank Set Sampling (RSS)

Two-Sample Scale Problem

Behrens-Fisher Problem

Paired Designs
Linear Models

Introduction

Geometry of Estimation and Tests

Examples

Assumptions for Asymptotic Theory

Theory of Rank-Based Estimates
Theory of Rank-Based Tests
Implementation of the R Analysis

L1 Analysis

Diagnostics

Survival Analysis

Correlation Model

High Breakdown (HBR) Estimates
Diagnostics for Differentiating between Fits

Rank-Based Procedures for Nonlinear Models
Experimental Designs: Fixed Effects
Introduction

One-Way Design
Multiple Comparison Procedures

Two-Way Crossed Factorial

Analysis of Covariance

Further Examples

Rank Transform
Models with Dependent Error Structure

Introduction

General Mixed Models

Simple Mixed Models
Arnold Transformations

General Estimating Equations (GEE)

Time Series
Multivariate
Multivariate Location Model

Componentwise

Spatial Methods

Affine Equivariant and Invariant Methods

Robustness of Estimates of Location

Linear Model

Experimental Designs
Appendix: Asymptotic Results
References
Index

Über Autor(innen)

Thomas P. Hettmansperger is a professor emeritus of statistics at Penn State University. Dr. Hettmansperger is a fellow of the American Statistical Association and Institute of Mathematical Statistics and an elected member of the International Statistical Institute. His research interests span nonparametric statistics, robust methods, and mixture models.
Joseph W. McKean is a professor of statistics at Western Michigan University. His research interests include robust nonparametric procedures for linear, nonlinear, and mixed models and times series designs. A fellow of the American Statistical Association, Dr. McKean has developed highly efficient and high breakdown procedures.