You are here

Disable VAT on Taiwan

Unfortunately, as of 1 January 2020 SAGE Ltd is no longer able to support sales of electronically supplied services to Taiwan customers that are not Taiwan VAT registered. We apologise for any inconvenience. For more information or to place a print-only order, please contact uk.customerservices@sagepub.co.uk.

Modern Methods for Robust Regression
Share
Share

Modern Methods for Robust Regression



September 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

 
 
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

Sample Materials & Chapters

Chapter 2

Chapter 4

Chapter 6

Andersen_files.zip


Robert Andersen

Robert Andersen is Professor of Business, Economics and Public Policy, and Professor of Strategy at the Ivey Business School, Western Univeristy. He is also cross-appointed in the Departments of Sociology, Political Science, and Statistics and Actuarial Science. His previous appointments include Distinguished Professor of Social Science at the University of Toronto, Senator William McMaster Chair in Political Sociology at McMaster University, and Senior Research Fellow at the University of Oxford. Andersen’s research expertise is in social statistics, social stratification, and political economy. Much of his recent research has explored... More About Author

Purchasing options

Please select a format:

ISBN: 9781412940726
$51.00

SAGE Research Methods is a research methods tool created to help researchers, faculty and students with their research projects. SAGE Research Methods links over 175,000 pages of SAGE’s renowned book, journal and reference content with truly advanced search and discovery tools. Researchers can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and more.