Pith. sign in

REVIEW 1 cited by

Asymptotic expansion of the weighted power variation with second order differences of a stochastic differential equation driven by fBm

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.03039 v1 pith:PFXCL2SL submitted 2024-07-03 math.PR

Asymptotic expansion of the weighted power variation with second order differences of a stochastic differential equation driven by fBm

classification math.PR
keywords expansiontheoryasymptoticdifferencesdifferentialdrivenequationformula
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We study a process satisfying a one-dimensional stochastic differential equation driven by fractional Brownian motion with Hurst index $H>1/2$, and consider the weighted power variation based on the second order differences of the process. We derive the asymptotic expansion formula of its distribution based on the theory of expansion of Skorohod integrals by Nualart and Yoshida. The formula includes the rate of convergence as a corollary. To facilitate the application of the general expansion theory, we employ the theory of exponents from arXiv:2407.02254 to obtain estimates of functionals.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation

    cs.LG 2026-05 unverdicted novelty 5.0

    FedNL reformulates federated learning as nested optimization with linear attention for collaborative test-time adaptation on non-IID data.