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Convergence rates in strong ergodicity for Markov processes

Author

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  • Mao, Yong-Hua

Abstract

A coupling method is used to obtain the explicit upper and lower bounds for convergence rates in strong ergodicity for Markov processes. For one-dimensional diffusion processes and birth-death processes, these bounds are sharp in the sense that the upper one and the lower one only differ in a constant.

Suggested Citation

  • Mao, Yong-Hua, 2006. "Convergence rates in strong ergodicity for Markov processes," Stochastic Processes and their Applications, Elsevier, vol. 116(12), pages 1964-1976, December.
    • Handle: RePEc:eee:spapps:v:116:y:2006:i:12:p:1964-1976
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    Citations

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    Cited by:

    1. Wang, Tao, 2022. "Ergodic convergence rates for time-changed symmetric Lévy processes in dimension one," Statistics & Probability Letters, Elsevier, vol. 183(C).
    2. Tomás Prieto-Rumeau & Onésimo Hernández-Lerma, 2016. "Uniform ergodicity of continuous-time controlled Markov chains: A survey and new results," Annals of Operations Research, Springer, vol. 241(1), pages 249-293, June.
    3. Shao, Jinghai, 2015. "Ergodicity of regime-switching diffusions in Wasserstein distances," Stochastic Processes and their Applications, Elsevier, vol. 125(2), pages 739-758.
    4. Yong-Hua Mao & Tao Wang, 2022. "Convergence Rates in Uniform Ergodicity by Hitting Times and $$L^2$$ L 2 -Exponential Convergence Rates," Journal of Theoretical Probability, Springer, vol. 35(4), pages 2690-2711, December.
    5. Guo, Chunyang & Liu, Yuanyuan, 2023. "Explicit Convergence Rates for the M/G/1 Queue under Perturbation," Applied Mathematics and Computation, Elsevier, vol. 458(C).

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