Estimating invertible processes in Hilbert spaces, with applications to functional ARMA processes

Sebastian Kühnert et al.

Bernoulli2026https://doi.org/10.3150/25-bej1918article
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Abstract

Invertible processes are central to functional time series analysis, making the estimation of their defining operators a key problem. While asymptotic error bounds have been established for specific ARMA models on L2[0,1], a general theoretical framework has not yet been considered. This paper fills in this gap by deriving consistent estimators for the operators characterizing the invertible representation of a functional time series with white noise innovations in a general separable Hilbert space. Under mild conditions covering a broad class of functional time series, we establish explicit asymptotic error bounds, with rates determined by operator smoothness and eigenvalue decay. These results further provide consistency-rate estimates for operators in Hilbert space-valued causal linear processes, including functional MA, AR, and ARMA models of arbitrary order.

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https://doi.org/https://doi.org/10.3150/25-bej1918

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@article{sebastian2026,
  title        = {{Estimating invertible processes in Hilbert spaces, with applications to functional ARMA processes}},
  author       = {Sebastian Kühnert et al.},
  journal      = {Bernoulli},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3150/25-bej1918},
}

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