A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning

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...

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1. Verfasser: Ziemann, Ingvar
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Learning
Computer Science - Systems and Control
Statistics - Machine Learning
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Beschreibung
Zusammenfassung: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.
DOI:10.48550/arxiv.2409.06437