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Approximation for the invariant measure with applications for jump processes (convergence in total variation distance)

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  • Bally, Vlad
  • Qin, Yifeng

Abstract

In this paper, we establish an abstract framework for the approximation of the invariant probability measure for a Markov semigroup. Following Pagès and Panloup (2022) we use an Euler scheme with decreasing step (unadjusted Langevin algorithm). Under some contraction property with exponential rate and some regularization properties, we give an estimate of the error in total variation distance. This abstract framework covers the main results in Pagès and Panloup (2022) and Chen et al. (2023). As a specific application we study the convergence in total variation distance to the invariant measure for jump type equations. The main technical difficulty consists in proving the regularization properties — this is done under an ellipticity condition, using Malliavin calculus for jump processes.

Suggested Citation

  • Bally, Vlad & Qin, Yifeng, 2024. "Approximation for the invariant measure with applications for jump processes (convergence in total variation distance)," Stochastic Processes and their Applications, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:spapps:v:176:y:2024:i:c:s0304414924001224
    DOI: 10.1016/j.spa.2024.104416
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    References listed on IDEAS

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    1. Chen, Peng & Deng, Chang-Song & Schilling, René L. & Xu, Lihu, 2023. "Approximation of the invariant measure of stable SDEs by an Euler–Maruyama scheme," Stochastic Processes and their Applications, Elsevier, vol. 163(C), pages 136-167.
    2. Kohatsu-Higa, Arturo & Tankov, Peter, 2010. "Jump-adapted discretization schemes for Lévy-driven SDEs," Stochastic Processes and their Applications, Elsevier, vol. 120(11), pages 2258-2285, November.
    3. Panloup, Fabien, 2008. "Computation of the invariant measure for a Lévy driven SDE: Rate of convergence," Stochastic Processes and their Applications, Elsevier, vol. 118(8), pages 1351-1384, August.
    4. Arnak S. Dalalyan, 2017. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 651-676, June.
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