Sage 出版社
http://www.sagepub.com/books/Book229716（介紹）  不是pdf

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.


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