A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning
<|reference_start|>A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning: In this note, we give a short information-theoretic proof of the consistency of the Gaussian maximum likelihood estimator in linear auto-regressive models. Our proof yields nearly optimal non-asymptotic rates for parameter recovery and works without any invocation of stability in the case of finite hypothesis classes.<|reference_end|>
@article{ziemann2024a,
    title={A Short Information-Theoretic Analysis of Linear Auto-Regressive
  Learning},
    author={Ingvar Ziemann},
    journal={arXiv preprint arXiv:2409.06437},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06437},
    primaryClass={cs.LG cs.SY eess.SY stat.ML}
}