A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.
Getting Started with R R Basics Reading External Data Generating Random Data Graphics Repeating Tasks User-Defined Functions Monte Carlo Simulation R Packages Basic Statistics Sign Test Signed-Rank Wilcoxon Bootstrap Robustness One- and Two-Sample Proportion Problems chi2 Tests Two-Sample Problems Introductory Example Rank-Based Analyses Scale Problem Placement Test for the Behrens-Fisher Problem Efficiency and Optimal Scores Adaptive Rank Scores Tests Regression I Simple Linear Regression Multiple Linear Regression Linear Models Aligned Rank Tests Bootstrap Nonparametric Regression Correlation ANOVA and ANCOVA One-Way ANOVA Multi-Way Crossed Factorial Design ANCOVA Methodology for Type III Hypotheses Testing Ordered Alternatives Multi-Sample Scale Problem Time-to-Event Analysis Kaplan-Meier and Log Rank Test Cox Proportional Hazards Models Accelerated Failure Time Models Regression II Robust Diagnostics Weighted Regression Linear Models with Skew Normal Errors A Hogg-Type Adaptive Procedure Nonlinear Time Series Cluster Correlated Data Friedman's Test Joint Rankings Estimator Robust Variance Component Estimators Multiple Rankings Estimator GEE-Type Estimator Bibliography Index Exercises appear at the end of each chapter.
