Modern Methods for Robust Regression
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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
- Defines key terms necessary to understanding the robustness of an estimator: Because they form the basis of robust regression techniques, the book also deals with various measures of location and scale.
- Addresses the robustness of validity and efficiency: After having described the robustness of validity for an estimator, the author discusses its efficiency.
- Focuses on the impact of outliers: The book compares the robustness of a wide variety of estimators that attempt to limit the influence of unusual observations.
- Gives an overview of some traditional techniques: Both formal statistical tests and graphical methods detect influential cases in the general linear model.
- Offers a Web appendix: This volume provides readers with the data and the R code for the examples used in the book.
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.
Learn more about "The Little Green Book" - QASS Series! Click Here
Contents
List of Figures
List of Figures
List of Tables
List of Tables
Series Editor's Introduction
Series Editor's Introduction
Acknowledgments
- 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
- 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
- 4. Robust Regression for the Linear Model
- 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
- 5. Standard Errors for Robust Regression
- Asymptotic Standard Errors for Robust Regression Estimators
- Bootstrapped Standard Errors
- Notes
- 6. Influential Cases in Generalized Linear Models
- The Generalized Linear Model
- Detecting Unusual Cases in Generalized Linear Models
- Robust Generalized Linear Models
- Notes
- 7. Conclusions
Appendix: Software Considerations for Robust Regression
Appendix: Software Considerations for Robust Regression
References
References
Index
Index
About the Author
About the Author
Description
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
- Defines key terms necessary to understanding the robustness of an estimator: Because they form the basis of robust regression techniques, the book also deals with various measures of location and scale.
- Addresses the robustness of validity and efficiency: After having described the robustness of validity for an estimator, the author discusses its efficiency.
- Focuses on the impact of outliers: The book compares the robustness of a wide variety of estimators that attempt to limit the influence of unusual observations.
- Gives an overview of some traditional techniques: Both formal statistical tests and graphical methods detect influential cases in the general linear model.
- Offers a Web appendix: This volume provides readers with the data and the R code for the examples used in the book.
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.
Learn more about "The Little Green Book" - QASS Series! Click Here
Contents
List of Figures
List of Figures
List of Tables
List of Tables
Series Editor's Introduction
Series Editor's Introduction
Acknowledgments
- 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
- 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
- 4. Robust Regression for the Linear Model
- 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
- 5. Standard Errors for Robust Regression
- Asymptotic Standard Errors for Robust Regression Estimators
- Bootstrapped Standard Errors
- Notes
- 6. Influential Cases in Generalized Linear Models
- The Generalized Linear Model
- Detecting Unusual Cases in Generalized Linear Models
- Robust Generalized Linear Models
- Notes
- 7. Conclusions
Appendix: Software Considerations for Robust Regression
Appendix: Software Considerations for Robust Regression
References
References
Index
Index
About the Author
About the Author
September 2007 | 128 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
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
- Defines key terms necessary to understanding the robustness of an estimator: Because they form the basis of robust regression techniques, the book also deals with various measures of location and scale.
- Addresses the robustness of validity and efficiency: After having described the robustness of validity for an estimator, the author discusses its efficiency.
- Focuses on the impact of outliers: The book compares the robustness of a wide variety of estimators that attempt to limit the influence of unusual observations.
- Gives an overview of some traditional techniques: Both formal statistical tests and graphical methods detect influential cases in the general linear model.
- Offers a Web appendix: This volume provides readers with the data and the R code for the examples used in the book.
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.
Learn more about "The Little Green Book" - QASS Series! Click Here
Table Of Contents:
- 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
- 2. Important Background
- 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
- 4. Robust Regression for the Linear Model
- 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
- 5. Standard Errors for Robust Regression
- Asymptotic Standard Errors for Robust Regression Estimators
- Bootstrapped Standard Errors
- Notes
- 6. Influential Cases in Generalized Linear Models
- The Generalized Linear Model
- Detecting Unusual Cases in Generalized Linear Models
- Robust Generalized Linear Models
- Notes
- 7. Conclusions
- Appendix: Software Considerations for Robust Regression
- References
- Index
- About the Author