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Mixture ensemble Kalman filters

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  • Frei, Marco
  • Künsch, Hans R.

Abstract

A generic algorithmic framework for nonlinear ensemble filtering based on Gaussian mixtures and fuzzy clustering techniques is introduced. The framework generalizes the ensemble Kalman filter and relaxes the assumption of a Gaussian prediction distribution. A theoretical analysis of the proposed procedure is provided, establishing strong consistency under suitable assumptions. Specific implementations are discussed and adjustments that are necessary in high-dimensional settings are proposed. A simple implementation of the filter is shown to work well in common testbeds, providing substantial gains over the ensemble Kalman filter.

Suggested Citation

  • Frei, Marco & Künsch, Hans R., 2013. "Mixture ensemble Kalman filters," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 127-138.
    • Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:127-138
    DOI: 10.1016/j.csda.2011.04.013
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    References listed on IDEAS

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    1. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    2. Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
    3. Furrer, Reinhard & Bengtsson, Thomas, 2007. "Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants," Journal of Multivariate Analysis, Elsevier, vol. 98(2), pages 227-255, February.
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    Cited by:

    1. Elizabeth Hou & Earl Lawrence & Alfred O Hero, 2021. "Penalized ensemble Kalman filters for high dimensional non-linear systems," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.

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