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
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.
This is an excellent text for intermediate and advanced
Quantitative Methods and Statistics courses offered at the graduate
level across the social sciences.
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