Modern Methods for Robust Regression

Häftad, Engelska, 2007

Del 152 i serien Quantitative Applications in the Social Sciences

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2007-11-12
Mått:139 x 215 x 8 mm
Vikt:140 g
Format:Häftad
Språk:Engelska
Serie:Quantitative Applications in the Social Sciences
Antal sidor:128
Upplaga:1
Förlag:SAGE Publications
ISBN:9781412940726

Utforska kategorier

Sociologi inom Samhälle och politik

Mer om författaren

Robert Andersen is Professor of Business, Economics and Public Policy, and Professor of Strategy at the Ivey Business School, Western Univeristy. He is also cross-appointed in the Departments of Sociology, Political Science, and Statistics and Actuarial Science. His previous appointments include Distinguished Professor of Social Science at the University of Toronto, Senator William McMaster Chair in Political Sociology at McMaster University, and Senior Research Fellow at the University of Oxford. Andersen’s research expertise is in social statistics, social stratification, and political economy. Much of his recent research has explored the cross-national relationships between economic conditions, especially income inequality, and a wide array of attitudes and behaviours important for liberal democracy and a successful business environment, including social trust, tolerance, civic participation, support for democracy and attitudes toward public policy. His published research includes Modern Methods for Robust Regression (Sage, 2008), and more than 70 academic papers including articles in the Annual Review of Sociology, American Journal of Political Science, American Sociological Review, British Journal of Political Science, British Journal of Sociology, Journal of Politics, Journal of the Royal Statistical Society, and Sociological Methodology. Andersen has provided consulting for the United Nations, the European Commission, the Canadian Government and the Council of Ministers of Education, Canada.

Innehållsförteckning

List of FiguresList of TablesSeries Editor′s IntroductionAcknowledgments1. IntroductionDefining RobustnessDefining Robust RegressionA Real-World Example: Coital Frequency of Married Couples in the 1970s2. Important BackgroundBias and ConsistencyBreakdown PointInfluence FunctionRelative EfficiencyMeasures of LocationMeasures of ScaleM-EstimationComparing Various EstimatesNotes3. Robustness, Resistance, and Ordinary Least Squares RegressionOrdinary Least Squares RegressionImplications of Unusual Cases for OLS Estimates and Standard ErrorsDetecting Problematic Observations in OLS RegressionNotes4. Robust Regression for the Linear ModelL-EstimatorsR-EstimatorsM-EstimatorsGM-EstimatorsS-EstimatorsGeneralized S-EstimatorsMM-EstimatorsComparing the Various EstimatorsDiagnostics Revisited: Robust Regression-Related Methods for Detecting OutliersNotes5. Standard Errors for Robust RegressionAsymptotic Standard Errors for Robust Regression EstimatorsBootstrapped Standard ErrorsNotes6. Influential Cases in Generalized Linear ModelsThe Generalized Linear ModelDetecting Unusual Cases in Generalized Linear ModelsRobust Generalized Linear ModelsNotes7. ConclusionsAppendix: Software Considerations for Robust RegressionReferencesIndexAbout the Author