Modern Methods for Robust Regression

by Robert Andersen

Overview

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.

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Product Details

ISBN-13:	9781412940726
Publisher:	SAGE Publications
Publication date:	09/06/2007
Series:	Quantitative Applications in the Social Sciences , #152
Edition description:	New Edition
Pages:	128
Product dimensions:	5.50(w) x 8.50(h) x (d)

About the Author

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 Mc Master Chair in Political Sociology at Mc Master 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 the cross-national relationships between economic conditions, especially income inequality, and a wide array of attitudes and behaviours important for liberal democracy and a successful business environment, including social trust, tolerance, civic participation, support for democracy and attitudes toward public policy. His published research includes Modern Methods for Robust Regression (Sage, 2008), and more than 70 academic papers including articles in the Annual Review of Sociology, American Journal of Political Science, American Sociological Review, British Journal of Political Science, British Journal of Sociology, Journal of Politics, Journal of the Royal Statistical Society, and Sociological Methodology. Andersen has provided consulting for the United Nations, the European Commission, the Canadian Government and the Council of Ministers of Education, Canada.

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