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Springer Verlag, Statistical Methods and Applications: Journal of the Italian Statistical Society, 3(19), p. 333-354

DOI: 10.1007/s10260-010-0133-0

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Infinitesimally Robust Estimation in General Smoothly Parametrized Models

Journal article published in 2009 by Matthias Kohl ORCID, Peter Ruckdeschel, Helmut Rieder
This paper is available in a repository.
This paper is available in a repository.

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Abstract

The aim of the paper is to give a coherent account of the robustness approach based on shrinking neighborhoods in the case of i.i.d. observations, and add some theoretical complements. An important aspect of the approach is that it does not require any particular model structure but covers arbitrary parametric models if only smoothly parametrized. In the meantime, equal generality has been achieved by object-oriented implementation of the optimally robust estimators. Exponential families constitute the main examples in this article. Not pretending a complete data analysis, we evaluate the robust estimates on real datasets from literature by means of our R packages ROptEst and RobLox.