Introduces Lie-Trotter and Strang splitting schemes for explicit pseudo-likelihood MLEs in SDEs with Hölder multiplicative noise, proving strong mean-square convergence, state preservation, consistency, and asymptotic normality of the LT estimator.
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Deterministic envelopes decouple stochastic-gradient noise from taming in SGLD, splitting stationary error into oracle-dependent bias and deterministic stabilization error, with a hybrid soft-hard design for far tails.
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Splitting schemes and estimators for stochastic differential equations with H\"older multiplicative noise
Introduces Lie-Trotter and Strang splitting schemes for explicit pseudo-likelihood MLEs in SDEs with Hölder multiplicative noise, proving strong mean-square convergence, state preservation, consistency, and asymptotic normality of the LT estimator.
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Deterministic Envelopes for Tamed SGLD: Decoupling Stochastic-Gradient Noise and Localizing Taming
Deterministic envelopes decouple stochastic-gradient noise from taming in SGLD, splitting stationary error into oracle-dependent bias and deterministic stabilization error, with a hybrid soft-hard design for far tails.