A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …

In robust estimation one frequently needs an initial or auxiliary estimate of scale. For this one
usually takes the median absolute deviation MAD n= 1.4826 med,{| xi− med jxj|}, because it …

This revised book provides a thorough explanation of the foundation of robust methods,
incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and …

The datasets used in statistical analyses are often small in the sense that the number of
observations n is less than 5 times the number of parameters p to be estimated. In contrast …

In this article we introduce a notion of depth in the regression setting. It provides the “rank” of
any line (plane), rather than ranks of observations or residuals. In simple regression we can …

When applying a statistical method in practice it often occurs that some observations deviate
from the usual assumptions. However, many classical methods are sensitive to outliers. The …

We use a robust regression estimator to analyze the risk premia on size and book‐to‐
market. We find that the risk premium on size that was estimated by Fama and French (1992) …

Equivariant high-breakdown point regression estimates are computationally expensive, and
the corresponding algorithms become unfeasible for moderately large number of regressors …

1 To the king, my lord, from your servant Balasi: 2... The king should have a look. Maybe the
scribe who reads to the king did not understand.... shall I personally show, with this tablet …

This paper introduces a new class of robust estimators for the linear regression model. They
are weighted least squares estimators, with weights adaptively computed using the …