{"product_id":"modern-methods-for-robust-regression-9781412940726","title":"Modern Methods for Robust Regression","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eModern Methods for Robust Regression\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003ci\u003eDefines key terms necessary to understanding the robustness of an estimator\u003c\/i\u003e: Because they form the basis of robust regression techniques, the book also deals with various measures of location and scale.\u003c\/li\u003e\n\u003cli\u003e\n\u003ci\u003eAddresses the robustness of validity and efficiency\u003c\/i\u003e: After having described the robustness of validity for an estimator, the author discusses its efficiency.\u003c\/li\u003e\n\u003cli\u003e\n\u003ci\u003eFocuses on the impact of outliers\u003c\/i\u003e: The book compares the robustness of a wide varie\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eList 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\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"SAGE Publications Inc","offers":[{"title":"Default Title","offer_id":51769438339415,"sku":"9781412940726","price":32.29,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781412940726.jpg?v=1758721092","url":"https:\/\/bookcurl.com\/products\/modern-methods-for-robust-regression-9781412940726","provider":"Book Curl","version":"1.0","type":"link"}