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Convergence rate of the EM algorithm for SDEs with low regular drifts
Published online by Cambridge University Press: 14 February 2022
Abstract
In this paper we employ a Gaussian-type heat kernel estimate to establish Krylov’s estimate and Khasminskii’s estimate for the Euler–Maruyama (EM) algorithm. For applications, by taking Zvonkin’s transformation into account, we investigate the convergence rate of the EM algorithm for a class of multidimensional stochastic differential equations (SDEs) with low regular drifts, which need not be piecewise Lipschitz.
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- Original Article
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- © The Author(s), 2022. Published by Cambridge University Press on behalf of Applied Probability Trust
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