Description

Book Synopsis

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 varie

    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

Modern Methods for Robust Regression

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    A Paperback by Robert Andersen

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      View other formats and editions of Modern Methods for Robust Regression by Robert Andersen

      Publisher: SAGE Publications Inc
      Publication Date: 1/12/2007 12:11:00 AM
      ISBN13: 9781412940726, 978-1412940726
      ISBN10: 1412940729

      Description

      Book Synopsis

      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 varie

        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

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