Robert Andersen - Böcker

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.

Perfect for any statistics student or researcher, this book offers hands-on guidance on how to interpret and discuss your results in a way that not only gives them meaning, but also achieves maximum impact on your target audience. No matter what variables your data involves, it offers a roadmap for analysis and presentation that can be extended to other models and contexts. Focused on best practices for building statistical models and effectively communicating their results, this book helps you: -        Find the right analytic and presentation techniques for your type of data -        Understand the cognitive processes involved in decoding information -        Assess distributions and relationships among variables -        Know when and how to choose tables or graphs -        Build, compare, and present results for linear and non-linear models -        Work with univariate, bivariate, and multivariate distributions -        Communicate the processes involved in and importance of your results.