Derives non-asymptotic MSE bounds separating discretization and fluctuation errors for expected signature estimation via block averaging under weak dependence for rough paths.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Rough-path market models satisfying no-controlled-free-lunch reduce admissible drivers to Itô lifts of Brownian motion (up to time change) once signature-type strategies are allowed.
General criteria extend L^p-mean Wasserstein convergence rates of occupation measures to non-stationary or non-Markovian ergodic processes under conditional convergence to equilibrium, with applications to Brownian diffusions and fractional Brownian driven SDEs.
citing papers explorer
-
Finite-Sample Bounds for Expected Signature Estimation under Weak Dependence
Derives non-asymptotic MSE bounds separating discretization and fluctuation errors for expected signature estimation via block averaging under weak dependence for rough paths.
-
Unbiased Rough Integrators and No Free Lunch in Rough-Path-Based Market Models
Rough-path market models satisfying no-controlled-free-lunch reduce admissible drivers to Itô lifts of Brownian motion (up to time change) once signature-type strategies are allowed.
-
Convergence rate of the occupation measure of classes of ergodic processes toward their invariant distribution in mean Wasserstein distance
General criteria extend L^p-mean Wasserstein convergence rates of occupation measures to non-stationary or non-Markovian ergodic processes under conditional convergence to equilibrium, with applications to Brownian diffusions and fractional Brownian driven SDEs.