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Robust weighted Gaussian processes

Author

Listed:
  • Ruben Ramirez-Padron

    (University of Central Florida
    TeleTracking Technologies)

  • Boris Mederos

    (Universidad Autónoma de Ciudad Juárez)

  • Avelino J. Gonzalez

    (University of Central Florida)

Abstract

This paper presents robust weighted variants of batch and online standard Gaussian processes (GPs) to effectively reduce the negative impact of outliers in the corresponding GP models. This is done by introducing robust data weighers that rely on robust and quasi-robust weight functions that come from robust M-estimators. Our robust GPs are compared to various GP models on four datasets. It is shown that our batch and online robust weighted GPs are indeed robust to outliers, significantly outperforming the corresponding standard GPs and the recently proposed heteroscedastic GP method GPz. Our experiments also show that our methods are comparable to and sometimes better than a state-of-the-art robust GP that uses a Student-t likelihood.

Suggested Citation

  • Ruben Ramirez-Padron & Boris Mederos & Avelino J. Gonzalez, 2021. "Robust weighted Gaussian processes," Computational Statistics, Springer, vol. 36(1), pages 347-373, March.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01011-0
    DOI: 10.1007/s00180-020-01011-0
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    References listed on IDEAS

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    1. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
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