## Book description

This book 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, which are available on CRAN. Each chapter includes exercises, making the book suitable for an undergraduate or graduate course.

1. Preliminaries
2. Preface
3. Chapter 1 Getting Started with R
1. 1.1 R Basics
3. 1.3 Generating Random Data
4. 1.4 Graphics
6. 1.6 User Defined Functions
7. 1.7 Monte Carlo Simulation
8. 1.8 R packages
9. 1.9 Exercises
4. Chapter 2 Basic Statistics
1. 2.1 Introduction
2. 2.2 Sign Test
3. 2.3 Signed-Rank Wilcoxon
4. 2.4 Bootstrap
5. 2.5 Robustness*
6. 2.6 One- and Two-Sample Proportion Problems
7. 2.7 χ2 Tests
1. 2.7.1 Goodness-of-Fit Tests for a Single Discrete Random Variable
2. 2.7.2 Several Discrete Random Variables
3. 2.7.3 Independence of Two Discrete Random Variables
4. 2.7.4 McNemar’s Test
8. 2.8 Exercises
5. Chapter 3 Two-Sample Problems
1. 3.1 Introductory Example
2. 3.2 Rank-Based Analyses
1. 3.2.1 Wilcoxon Test for Stochastic Ordering of Alternatives
2. 3.2.2 Analyses for a Shift in Location
3. 3.2.3 Analyses Based on General Score Functions
4. 3.2.4 Linear Regression Model
3. 3.3 Scale Problem
4. 3.4 Placement Test for the Behrens–Fisher Problem
5. 3.5 Efficiency and Optimal Scores*
6. 3.6 Adaptive Rank Scores Tests
7. 3.7 Exercises
6. Chapter 4 Regression I
1. 4.1 Introduction
2. 4.2 Simple Linear Regression
3. 4.3 Multiple Linear Regression
4. 4.4 Linear Models*
5. 4.5 Aligned Rank Tests*
6. 4.6 Bootstrap
7. 4.7 Nonparametric Regression
8. 4.8 Correlation
9. 4.9 Exercises
7. Chapter 5 ANOVA and ANCOVA
1. 5.1 Introduction
2. 5.2 One-Way ANOVA
3. 5.3 Multi-Way Crossed Factorial Design
4. 5.4 ANCOVA*
1. 5.4.1 Computation of Rank-Based ANCOVA
5. 5.5 Methodology for Type III Hypotheses Testing*
6. 5.6 Ordered Alternatives
7. 5.7 Multi-Sample Scale Problem
8. 5.8 Exercises
8. Chapter 6 Time to Event Analysis
1. 6.1 Introduction
2. 6.2 Kaplan–Meier and Log Rank Test
3. 6.3 Cox Proportional Hazards Models
4. 6.4 Accelerated Failure Time Models
5. 6.5 Exercises
9. Chapter 7 Regression II
1. 7.1 Introduction
2. 7.2 High Breakdown Rank-Based Fits
1. Stars Data
3. 7.3 Robust Diagnostics
4. 7.4 Weighted Regression
5. 7.5 Linear Models with Skew Normal Errors
6. 7.6 A Hogg-Type Adaptive Procedure
7. 7.7 Nonlinear
1. 7.7.1 Implementation of the Wilcoxon Nonlinear Fit
2. 7.7.2 R Computation of Rank-Based Nonlinear Fits
3. 7.7.3 Examples
4. 7.7.4 High Breakdown Rank-Based Fits
8. 7.8 Time Series
9. 7.9 Exercises
10. Chapter 8 Cluster Correlated Data
1. 8.1 Introduction
2. 8.2 Friedman’s Test
3. 8.3 Joint Rankings Estimator
1. 8.3.1 Estimates of Standard Error
2. 8.3.2 Inference
3. 8.3.3 Examples
4. 8.4 Robust Variance Component Estimators
5. 8.5 Multiple Rankings Estimator
6. 8.6 GEE-Type Estimator
7. 8.7 Exercises
11. Bibliography

## Product information

• Title: Nonparametric Statistical Methods Using R
• Author(s): John Kloke, Joseph McKean
• Release date: October 2014
• Publisher(s): Chapman and Hall/CRC
• ISBN: 9781498787277