Outlier detection in state-space models using mean-shift penalisation.

Authors

Rajan Shankar, Ines Wilms, Jakob Raymaekers, Garth Tarr

Published in

Statistics and computing. Volume 36. Issue 4. Pages 176. Epub Jul 13, 2026.

Abstract

State-space models (SSMs) provide a flexible framework for modelling time series data, but their reliance on Gaussian error assumptions makes them highly sensitive to outliers. We propose a robust estimation method, ROAMS, that mitigates the influence of additive outliers by introducing shift parameters at each timepoint in the observation equation of the SSM. These parameters allow the model to attribute non-zero shifts to outliers while leaving clean observations unaffected. ROAMS then enables automatic outlier detection, through the addition of a penalty term on the number of flagged outlying timepoints in the loss function, and simultaneous estimation of model parameters. We apply the method to robustly estimate SSMs on both simulated data and real-world animal location-tracking data, demonstrating its ability to produce more reliable parameter estimates than classical methods and other benchmark methods. In addition to improved robustness, ROAMS offers practical diagnostic tools, including BIC curves for selecting tuning parameters and visualising outlier structure. These features make our approach broadly useful for researchers and practitioners working with contaminated time series data.
The online version contains supplementary material available at 10.1007/s11222-026-10935-4.

PMID:
42454343
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.

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