High Breakdown-Point and High Efficiency Robust Estimates for Regression

High Breakdown Point Robust Regression with Censored Data

In this paper, we propose a class of high breakdown point estimators for the linear regression model when the response variable contains censored observations. These estimators are robust against high-leverage outliers and they generalize the LMS (least median of squares), S, MM and τ -estimators for linear regression. An important contribution of this paper is that we can define consistent est...

High Breakdown Point Estimation in Regression

In robust regression theory the estimators, which can “resist” contamination of nearly fifty percent of the data, due to the fact that they are highly important in practice, were intensively studied. In this paper we describe three methods with high breakdown point: the least trimmed squares (LTS), the least median of squares (LMS) and their generalization the least weighted squares (LWS) estim...

Econometric applications of high - breakdown robust regression techniques

A literature search shows that robust regression techniques are rarely used in applied econometrics. We present a technique based on Rousseeuw and Van Zomeren [Journal of the American Statistical Association, 85 (1990) 633–639] that removes many of the difficulties in applying such techniques to economic data. We demonstrate the value of these techniques by re-analyzing three OLS-based regressi...

High-Breakdown Robust Multivariate Methods

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 goal of robust statistics is to develop methods that are robust against the possibility that one or several unannounced outliers may occur anywhere in the data. These methods then allow to detect outlying observa...

High Breakdown Rank-Based Estimates for Linear Models