The words `robust' and `resistant'. In regression analysis, to employ both multiple robust methods and least squares to compare the results. However, many of the popular modern robust regression techniques, which. The most common general method of robust regression is M-estimation, introduced by Hu. Modern methods for robust regression pdf Modern Methods for Robust Regression. Sage.Researchers need to consider robust estimation methods when analyzing data in multiple regression. Textbooks articulate many different robust regression methods, however, only a few.
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Series:
Quantitative Applications in the Social Sciences
September 2007 | 128 pages | SAGE Publications, Inc
Modern Methods for Robust Regression offers a brief but in-depth treatment of various methods for detecting and properly handling influential cases in regression analysis. This volume, geared toward both future and practicing social scientists, is unique in that it takes an applied approach and offers readers empirical examples to illustrate key concepts. It is ideal for readers who are interested in the issues related to outliers and influential cases.
Key Features
Intended Audience
This is an excellent text for intermediate and advanced Quantitative Methods and Statistics courses offered at the graduate level across the social sciences.
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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
Relative Efficiency
Measures of Location
Measures of Scale
M-Estimation
Comparing Various Estimates
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
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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
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