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arXiv 2201.10158math.PR
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重尾采样下$α$ -稳定过程驱动的超临界随机微分方程的遍历性

Ergodicity of supercritical SDEs driven by $α$-stable processes and heavy-tailed sampling

Xiaolong Zhang, Xicheng Zhang

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摘要：

设$α\in(0,2)$和$d\in\mathbb{N}$。考虑以下由$α$驱动的随机微分方程（SDE）-$\mathbb{R}^d$中的稳定过程：$$ dX_t=b(X_t)dt+σ(X_{t-})d L^α_t, \quad X_0=x\in\mathbb{R}^d, $$其中$b:\mathbb{R}^d\to\mathbb{R}^d$和$σ:\mathbb{R}^d\to\mathbb{R}^d\otimes\mathbb{R}^d$与$γ\in(0\vee(1-α)^+,1]$局部$γ$ -Hölder连续，$L^α_t$是$d$维旋转不变$α$ -稳定过程。在$b,σ$的耗散和非退化假设下，证明了与$(X_t(x),t\geq 0)$相关的半群$P_t$的$V$ -一致指数遍历性.我们的证明主要基于最近在\cite{MZ20}中建立的热核估计，通过证明$P_t$的强Feller性质和不可约性。有趣的是，当$α$变为零时，扩散系数$σ$可以比漂移$b$增长得更快。作为应用，我们提出了一种新的重尾抽样方案。

英文摘要：

Let $α\in(0,2)$ and $d\in\mathbb{N}$. Consider the following stochastic differential equation (SDE) driven by $α$-stable process in $\mathbb{R}^d$: $$ dX_t=b(X_t)dt+σ(X_{t-})d L^α_t, \quad X_0=x\in\mathbb{R}^d, $$ where $b:\mathbb{R}^d\to\mathbb{R}^d$ and $σ:\mathbb{R}^d\to\mathbb{R}^d\otimes\mathbb{R}^d$ are locally $γ$-Hölder continuous with $γ\in(0\vee(1-α)^+,1]$, $L^α_t$ is a $d$-dimensional rotationally invariant $α$-stable process. Under some dissipative and non-degenerate assumptions on $b,σ$, we show the $V$-uniformly exponential ergodicity for the semigroup $P_t$ associated with $(X_t(x),t\geq 0)$. Our proofs are mainly based on the heat kernel estimates recently established in \cite{MZ20} through showing the strong Feller property and the irreducibility of $P_t$. It is interesting that when $α$ goes to zero, the diffusion coefficient $σ$ can grow faster than drift $b$. As applications, we put forward a new heavy-tailed sampling scheme.

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