Introduction to Robust Estimation and Hypothesis Testing

Introduction to Robust Estimation and Hypothesis Testing, 4th Edition, is a textbook that explains how to apply robust statistical techniques using specialized software.

The volume addresses methods for managing outliers, skewed distribution curvature, and heteroscedasticity while providing gains in statistical power. Core subjects include robust estimation of location and scale, density estimators, trimmed means, and group comparisons.

Designed for individuals studying associations, comparing groups, or working with multivariate data, the book assumes basic prior training in statistics. Real-world study data and an accompanying R package with over 1,200 functions support practical implementation.