Robust Filtering

Laurent Calvet, Veronika Czellar and Elvezio Ronchetti
Additional contact information
Veronika Czellar: EM - EMLyon Business School
Elvezio Ronchetti: UNIGE - Université de Genève = University of Geneva

Post-Print from HAL

Abstract: Filtering methods are powerful tools to estimate the hidden state of a statespace model from observations available in real time. However, they are known to be highly sensitive to the presence of small misspecifications of the underlying model and to outliers in the observation process. In this paper, we show that the methodology of robust statistics can be adapted to sequential filtering. We define a filter as being robust if the relative error in the state distribution caused by misspecifications is uniformly bounded by a linear function of the perturbation size. Since standard filters are nonrobust even in the simplest cases, we propose robustified filters which provide accurate state and parameter inference in the presence of model misspecifications. In particular, the robust particle filter naturally mitigates the degeneracy problems that plague the bootstrap particle filter (Gordon, Salmond and Smith, 1993) and its many extensions. We illustrate the good properties of robust filters in linear and nonlinear state-space examples.

Keywords: Kalman filter; Particle filter; Robust statistics; State-space model; Weight degeneracy (search for similar items in EconPapers)
Date: 2015-10-01
References: Add references at CitEc
Citations:

Published in Journal of the American Statistical Association, 2015, 110 (512), 1591-1606 p. ⟨10.1080/01621459.2014.983520⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02313229

DOI: 10.1080/01621459.2014.983520

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().