# Non asymptotic estimation lower bounds for LTI state space models with Cram'er-Rao and van Trees

arXiv: https://arxiv.org/abs/2109.08582 · HTML: https://mlsanity.com/paper/2109.08582/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification](https://mlsanity.com/paper/1802.08334/) (2018)
- [Near optimal finite time identification of arbitrary linear dynamical systems](https://mlsanity.com/paper/1812.01251/) (2018)
- [Finite Time Identification in Unstable Linear Systems](https://mlsanity.com/paper/1710.01852/) (2017)
- [Sample Complexity Lower Bounds for Linear System Identification](https://mlsanity.com/paper/1903.10343/) (2019)

## Built on by (most cited first, 3 of 3)

- [Finite-sample analysis of identification of switched linear systems with arbitrary or restricted switching](https://mlsanity.com/paper/2203.09862/) (2022)
- [Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds](https://mlsanity.com/paper/2405.06089/) (2024)
- [Near-optimal Rank Adaptive Inference of High Dimensional Matrices](https://mlsanity.com/paper/2510.08117/) (2025)

## Abstract (arXiv)

We study the estimation problem for linear time-invariant (LTI) state-space models with Gaussian excitation of an unknown covariance. We provide non asymptotic lower bounds for the expected estimation error and the mean square estimation risk of the least square estimator, and the minimax mean square estimation risk. These bounds are sharp with explicit constants when the matrix of the dynamics has no eigenvalues on the unit circle and are rate-optimal when they do. Our results extend and improve existing lower bounds to lower bounds in expectation of the mean square estimation risk and to systems with a general noise covariance. Instrumental to our derivation are new concentration results for rescaled sample covariances and deviation results for the corresponding multiplication processes of the covariates, a differential geometric construction of a prior on the unit operator ball of small Fisher information, and an extension of the Cram'er-Rao and van Treesinequalities to matrix-valued estimators.