On necessary and sufficient conditions for the local large deviation principle
Konstantin Borovkov

TL;DR
This paper establishes nearly equivalent necessary and sufficient conditions for the local large deviation principle (LLDP) for random vectors, linking it to the existence of a specific limit involving exponential moments and the Legendre--Fenchel transform.
Contribution
It provides a relaxed, near-equivalent characterization of LLDP conditions that avoids restrictive integrability assumptions, advancing understanding of large deviation principles.
Findings
Necessary and sufficient conditions for LLDP are closely aligned.
The existence of a specific limit involving exponential moments characterizes LLDP.
The conditions relate the rate function to the Legendre--Fenchel transform of a limit.
Abstract
One says that the local large deviation principle (LLDP) is satisfied for a family of random vectors in if there exists a function such that, for any , \[ \lim_{T\to \infty}T^{-1}\ln \mathbf{P} (|\zeta_T -\alpha|<\varepsilon_T)= - D(\alpha)\] for slowly enough. In this paper, we establish necessary and sufficient conditions for the LLDP that are very close to each other. Namely, if the LLDP is satisfied then, for slowly enough as , there exists the limit \[ A(\mu):= \lim_{T\to\infty}T^{-1}\ln \mathbf{E} (e^{T\langle \mu, \zeta_T\rangle}; |\zeta_T|\le M_T)\in (-\infty, \infty],\quad \mu\in \mathbb R^d,\] which is equal to the Legendre--Fenchel transform of the rate function . Conversely, if the above…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
