Modern methods for robust regression

著者

• Andersen, Robert, Ph. D

書誌事項

Modern methods for robust regression

Robert Andersen

（Sage publications series, . Quantitative applications in the social sciences ; no.07-152）

Sage Publications, c2008

• : pbk

注記

Includes bibliographical references (p. 95-99) and index

内容説明・目次

内容説明

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.

目次

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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詳細情報

• NII書誌ID(NCID): BA83392659
• ISBN:
  • 9781412940726
• LCCN: 2007009019
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Thousand Oaks, Calif.
• ページ数/冊数: xv, 107 p.
• 大きさ: 22 cm
• 分類:
  • LCC : HA31.3
  • DC22 : 519.5/36
• 件名:
  • LCSH : Social sciences -- Statistical methods
  • LCSH : Regression analysis
  • LCSH : Robust statistics
• 親書誌ID:
  • BA71788007