Robustness: From Basic Concepts to Robust Filtering (Elvezio Ronchetti, University of Geneva)

08.10.2015 16:15

Robust statistics deals with deviations from ideal models and develops statistical procedures which are still reliable and reasonablyefficient in a small neighborhood of the model.
We first review some fundamental ideas developed in robust statistics which can be used to construct robust statistical procedures in fairly general settings.
We then adapt these ideas to filtering methods, which are powerful tools to estimate the hidden state of a state-space model, by defining a concept of robustness for a filter and by proposing robust filters which provide accurate state and parameter inference in the presence of model misspecifications.
Joint work with L. Calvet and V. Czellar.

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