Modern Methods for Robust Regression

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 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.

• List of FiguresList of TablesSeries Editor′s IntroductionAcknowledgments1. IntroductionDefining RobustnessDefining Robust RegressionA Real-World Example: Coital Frequency of Married Couples in the 1970s2. Important BackgroundBias and ConsistencyBreakdown PointInfluence FunctionRelative EfficiencyMeasures of LocationMeasures of ScaleM-EstimationComparing Various EstimatesNotes3. Robustness, Resistance, and Ordinary Least Squares RegressionOrdinary Least Squares RegressionImplications of Unusual Cases for OLS Estimates and Standard ErrorsDetecting Problematic Observations in OLS RegressionNotes4. Robust Regression for the Linear ModelL-EstimatorsR-EstimatorsM-EstimatorsGM-EstimatorsS-EstimatorsGeneralized S-EstimatorsMM-EstimatorsComparing the Various EstimatorsDiagnostics Revisited: Robust Regression-Related Methods for Detecting OutliersNotes5. Standard Errors for Robust RegressionAsymptotic Standard Errors for Robust Regression EstimatorsBootstrapped Standard ErrorsNotes6. Influential Cases in Generalized Linear ModelsThe Generalized Linear ModelDetecting Unusual Cases in Generalized Linear ModelsRobust Generalized Linear ModelsNotes7. ConclusionsAppendix: Software Considerations for Robust RegressionReferencesIndexAbout the Author