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The robust approach to data analysis uses models that do not completely specify the distribution of the data, but rather assume that this distribution belongs to a certain neighborhood of a parametric model. Consequently, robust inference should be valid under all the distributions in these neighborhoods. Regarding robust inference, there are two important(More)
1 ABSTRACT The maximum asymptotic bias of an estimator is a global robustness measure of the performance of an estimator. The projection median estimator for multivariate location shows a remarkable behavior regarding asymptotic bias. In this paper we consider a modiication of the projection median es-timator which renders an estimate with better bias(More)
It is well known that when the data may contain outliers or other departures from the assumedmodel, classical inferencemethods can be seriously affected and yield confidence levels much lower than the nominal ones. This paper proposes robust confidence intervals and tests for the parameters of the simple linear regression model that maintain their coverage(More)
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