Robustness in Statistics

Robert L. Launer, Graham N. Wilkinson

• Publisher: Academic Press
• Year: 2014
• ISBN:
  9781483263366
• Topic: Mathematics
• Subtopic: Applied Mathematics
• Index: Mathematics

Robustness in Statistics contains the proceedings of a Workshop on Robustness in Statistics held on April 11-12, 1978, at the Army Research Office in Research Triangle Park, North Carolina. The papers review the state of the art in statistical robustness and cover topics ranging from robust estimation to the robustness of residual displays and robust smoothing. The application of robust regression to trajectory data reduction is also discussed. Comprised of 14 chapters, this book begins with an introduction to robust estimation, paying particular attention to iteration schemes and error structure of estimators. Sensitivity and influence curves as well as their connection with jackknife estimates are described. The reader is then introduced to a simple analog of trimmed means that can be used for studying residuals from a robust point-of-view; a class of robust estimators (called P-estimators) based on the location and scale-invariant Pitman estimators of location; and robust estimation in the presence of outliers. Subsequent chapters deal with robust regression and its use to reduce trajectory data; tests for censoring of extreme values, especially when population distributions are incompletely defined; and robust estimation for time series autoregressions. This monograph should be of interest to mathematicians and statisticians.

Table of contents

• Front Cover
• Robustness in Statistics
• Copyright Page
• Table of Contens
• CONTRIBUTORS
• PREFACE
• ABSTRACTS
• Chapter 1. An Introduction to Robust Estimation
• Chapter 2. The Robustness of Residual Displays
• Chapter 3. Robust Smoothing
• Chapter 4. Robust Pitman-like Estimators
• CHapter 5. Robust Estimation in the Presence of Outliers
• Chapter 6. Study of Robustness by Simulation: Particularly Improvement by Adjustment and Combination
• Chapter 7. Robust Techniques for the User
• CHapter 8. Application of Robust Regression to Trajectory Data Reduction
• Chapter 9. Tests for Censoring of Extreme Values (Especially) When Population Distributions Are Incompletely Defined
• Chapter 10. Robust Estimation for Time Series Autoregressions
• Chapter 11. Robust Techniques in Communication
• Chapter 12. Robustness in the Strategy of Scientific Model Building
• Chapter 13. A Density–Quantile Function Perspective on Robust Estimation
• Chapter 14. Robust Inference—The Fisherian Approach
• AUTHOR INDEX
• SUBJECT INDEX