Modern Methods for Robust Regression (Quantitative Applications in the Social Sciences)
By: Robert Andersen (author)Paperback
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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.
Robert Andersen is a Professor of Sociology and Political Science at the University of Toronto. His research interests are in applied statistics, political sociology (especially the social bases of attitudes and political behavior), social stratification, and the sociology of work. Some of his recent work has appeared in the American Sociological Review, the Journal of Politics, and Sociological Methodology.
List of FiguresList of TablesSeries Editor's IntroductionAcknowledgments1. Introduction Defining Robustness Defining Robust Regression A Real-World Example: Coital Frequency of Married Couples in the 1970s2. Important Background Bias and Consistency Breakdown Point Influence Function Relative Efficiency Measures of Location Measures of Scale M-Estimation Comparing Various Estimates Notes3. 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 Notes4. 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 Notes5. Standard Errors for Robust Regression Asymptotic Standard Errors for Robust Regression Estimators Bootstrapped Standard Errors Notes6. Influential Cases in Generalized Linear Models The Generalized Linear Model Detecting Unusual Cases in Generalized Linear Models Robust Generalized Linear Models Notes7. ConclusionsAppendix: Software Considerations for Robust RegressionReferencesIndexAbout the Author
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- ID: 9781412940726
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