Introduction to Robust Estimation and Hypothesis Testing (Statistical Modeling and Decision Science)

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

• Observation and Experiment: An Introduction to Causal Inference
• Introduction to Mediation, Moderation, and Conditional Process Analysis, First Edition: A Regression-Based Approach (Methodology in the Social Sciences)
• Regression Basics
• Applied Multivariate Statistical Concepts
• Recommender Systems: The Textbook
• The Theory and Practice of Item Response Theory (Methodology in the Social Sciences)
• An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo (The MIT Press)
• Latent Variable Modeling with R
• Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
• How to Think About Analysis