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Modern Methods for Robust Regression

Modern Methods for Robust Regression

November 2007 | 128 pages | SAGE Publications, Inc
Geared towards both future and practising social scientists, this book takes an applied approach and offers readers empirical examples to illustrate key concepts. It includes: applied coverage of a topic that has traditionally been discussed from a theoretical standpoint; empirical examples to illustrate key concepts; a web appendix that provides readers with the data and the R-code for the examples used in the book.
List of Figures
List of Tables
Series Editor's Introduction
1. Introduction
Defining Robustness

Defining Robust Regression

A Real-World Example: Coital Frequency of Married Couples in the 1970s

2. Important Background
Bias and Consistency

Breakdown Point

Influence Function

Relative Efficiency

Measures of Location

Measures of Scale


Comparing Various Estimates


3. Robustness, Resistance, and Ordinary Least Squares Regression
Ordinary Least Squares Regression

Implications of Unusual Cases for OLS Estimates and Standard Errors

Detecting Problematic Observations in OLS Regression


4. Robust Regression for the Linear Model





Generalized S-Estimators


Comparing the Various Estimators

Diagnostics Revisited: Robust Regression-Related Methods for Detecting Outliers


5. Standard Errors for Robust Regression
Asymptotic Standard Errors for Robust Regression Estimators

Bootstrapped Standard Errors


6. Influential Cases in Generalized Linear Models
The Generalized Linear Model

Detecting Unusual Cases in Generalized Linear Models

Robust Generalized Linear Models


7. Conclusions
Appendix: Software Considerations for Robust Regression
About the Author

Sample Materials & Chapters

Chapter 2

Chapter 4

Chapter 6

For instructors

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