Robust nonparametric statistical methods

✍ Scribed by Thomas P Hettmansperger; Joseph W McKean

• Publisher: CRC Press
• Year: 2011
• Leaves: 532
• Series: Monographs on statistics and applied probability (Series), 119
• Edition: 2nd ed
• Category: Library

✦ Synopsis

"Often referred to as distribution-free methods, nonparametric methods do not rely on assumptions that the data are drawn from a given probability distribution. With an emphasis on Wilcoxon rank methods that enable a unified approach to data analysis, this book presents a unique overview of robust nonparametric statistical methods. Drawing on examples from various disciplines, the relevant R code for these examples, as well as numerous exercises for self-study, the text covers location models, regression models, designed experiments, and multivariate methods. This edition features a new chapter on cluster correlated data"-- Read more...

✦ Table of Contents

Content: 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
Abstract: "Often referred to as distribution-free methods, nonparametric methods do not rely on assumptions that the data are drawn from a given probability distribution. With an emphasis on Wilcoxon rank methods that enable a unified approach to data analysis, this book presents a unique overview of robust nonparametric statistical methods. Drawing on examples from various disciplines, the relevant R code for these examples, as well as numerous exercises for self-study, the text covers location models, regression models, designed experiments, and multivariate methods. This edition features a new chapter on cluster correlated data"