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Control variates for dry friction under colored noise: variance rates for non-smooth observables
arXiv stat.CO0 views
arXiv:2610.27765v1 Announce Type: cross Abstract: A control variate method was introduced by Garnier and Mertz (2022) for multivalued stochastic differential equations driven by colored noise. For smooth observables, the asymptotic variance is $O(\varepsilon^{2})$, where $\varepsilon^{2}$ is the correlation time of the noise. However, for non-smooth reliability observables, such as threshold indicators and overshoot severities, rate estimates have not been established in general. In this paper, rate estimates are derived for the inertial dry-friction inclusion driven by colored noise in the three regimes whose white-noise propagators are known. The asymptotic variance of the resulting control variate estimators is $O(\varepsilon^{2\alpha})$ for $\alpha$-H\"older observables with $\alpha\in(0,1]$, $O(\varepsilon)$ for observables of bounded variation satisfying a one-sided continuity condition, $O(\varepsilon^{1+\alpha})$ for observables that are both $\alpha$-H\"older and of bounded variation, and $O(\varepsilon^{\min\{2,1+2\alpha\}})$ for observables with finitely many $\alpha$-cusps, where $\alpha \in (0, 1]$ is the cusp exponent. The last three bounds hold up to logarithmic factors. Bounds on the bias of the white-noise approximation and non-asymptotic sample-size estimates are also derived, and the comparison principle is shown to be preserved under coupled time discretization. Numerical experiments in the three regimes support the predicted rates.
Source:https://arxiv.org/abs/2610.27765PreviousDivide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process ModelsNextDirect Inversion for Chi-Square Sampling, the Squared Bessel Process, and Applications
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