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Modern Methods for Robust Regression

Robert Andersen
Livre broché | Anglais | Quantitative Applications in the Social Sciences | n° 152
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Description

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.

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Spécifications

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Editeur:

Contenu

Nombre de pages :
128
Langue:
Anglais
Collection :
Tome:
n° 152

Caractéristiques

EAN:
9781412940726
Date de parution :
06-09-07
Format:
Livre broché
Format numérique:
Trade paperback (VS)
Dimensions :
143 mm x 215 mm
Poids :
154 g

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