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Modern Methods for Robust Regression de Robert Andersen

Modern Methods for Robust Regression: Quantitative Applications in the Social Sciences, cartea 152

Autor Robert Andersen
en Limba Engleză Electronic book text – 29 sep 2007
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.
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Specificații

ISBN-13: 9781452213200
ISBN-10: 1452213208
Pagini: 128
Dimensiuni: 140 x 216 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences

Locul publicării:Thousand Oaks, United States

Cuprins

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

Notă biografică