Regularization of differential equations by fractional noise

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Wiener integrals, Malliavin calculus and covariance measure structure (2006, unknown): “• the proof of the Girsanov transform and the use of it to the study of stochastic equations driven by Gaussian noises, as e.g. in [31].”
Averaging along irregular curves and regularisation of ODEs (2012, background): “As bn and Xn are regular enough, according to Nualart and Ouknine [11], there exist a Brownian motion B related to the filtration associated with W and a probability Pn such that the process (X n t )t∈[0,TGir ] is a fractional Brownian motion of Hurst parameter H, where dPn dP = exp ( − ∫ TGir”
Noiseless regularisation by noise (2020, methodology): “α-stable and log regular processes) [1, 35, 41, 45, 16].”
Statistical aspects of the fractional stochastic calculus (2006, methodology): “Our MLE construction is based on the following observation (see [31]).”
A stochastic sewing lemma and applications (2018, methodology): “Nualart and Ouknine in [37] obtain strong existence and pathwise uniqueness for (6.”
Distribution dependent SDEs driven by additive fractional Brownian motion (2021, methodology): “As already mentioned, Girsanov transform holds for fBm, see [35]; suﬃcient conditions in order to apply it in our context (in particular to check that Novikov condition is satisﬁed) can be found in [17, Section 4.2.2], to which we also refer for more details on the explicit formula for d P / d Q .”