These are robust methods, such as Least Median of Squares (LMS), Least Trimmed Squares (LTS), Huber M Estimation, MM Estimation, Least Absolute Value Method (LAV) and S Estimation 3, 4, 18, 20. The main purpose of robust regression is to provide resistant results in the presence of outliers.
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Quantitative Applications in the Social Sciences
Quantitative Applications in the Social Sciences
Other Titles in:
Quantitative/Statistical Research (General) | Regression & Correlation | Research Methods & Evaluation (General)
Quantitative/Statistical Research (General) | Regression & Correlation | Research Methods & Evaluation (General)
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
Acknowledgments
1. Introduction
Defining Robustness
Defining Robust Regression
A Real-World Example: Coital Frequency of Married Couples in the 1970s
Bias and Consistency
Breakdown Point
Influence Function
![Pdf Pdf](/uploads/1/2/6/3/126372396/999726144.png)
Relative Efficiency
Measures of Location
Measures of Scale
M-Estimation
Comparing Various Estimates
![Squared Squared](http://www.frontiersin.org/files/Articles/163300/fams-01-00014-HTML-r2/image_m/fams-01-00014-t008.jpg)
Notes
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
Notes
L-Estimators
R-Estimators
M-Estimators
GM-Estimators
S-Estimators
Generalized S-Estimators
MM-Estimators
Comparing the Various Estimators
Diagnostics Revisited: Robust Regression-Related Methods for Detecting Outliers
Notes
Asymptotic Standard Errors for Robust Regression Estimators
Bootstrapped Standard Errors
Notes
The Generalized Linear Model
Detecting Unusual Cases in Generalized Linear Models
Robust Generalized Linear Models
Notes
Appendix: Software Considerations for Robust Regression
References
Index
About the Author