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 FeaturesDefines key terms necessary to understanding the robustness of an estimator: Because they form the basis of robust regression…mehr
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 FeaturesDefines 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
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.
Inhaltsangabe
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
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
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