Robustness of iterated function systems of Lipschitz maps

IF 0.7 4区 数学 Q3 STATISTICS & PROBABILITY
L. Hervé, J. Ledoux
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引用次数: 1

Abstract

Abstract Let $\{X_n\}_{n\in{\mathbb{N}}}$ be an ${\mathbb{X}}$ -valued iterated function system (IFS) of Lipschitz maps defined as $X_0 \in {\mathbb{X}}$ and for $n\geq 1$ , $X_n\;:\!=\;F(X_{n-1},\vartheta_n)$ , where $\{\vartheta_n\}_{n \ge 1}$ are independent and identically distributed random variables with common probability distribution $\mathfrak{p}$ , $F(\cdot,\cdot)$ is Lipschitz continuous in the first variable, and $X_0$ is independent of $\{\vartheta_n\}_{n \ge 1}$ . Under parametric perturbation of both F and $\mathfrak{p}$ , we are interested in the robustness of the V-geometrical ergodicity property of $\{X_n\}_{n\in{\mathbb{N}}}$ , of its invariant probability measure, and finally of the probability distribution of $X_n$ . Specifically, we propose a pattern of assumptions for studying such robustness properties for an IFS. This pattern is implemented for the autoregressive processes with autoregressive conditional heteroscedastic errors, and for IFS under roundoff error or under thresholding/truncation. Moreover, we provide a general set of assumptions covering the classical Feller-type hypotheses for an IFS to be a V-geometrical ergodic process. An accurate bound for the rate of convergence is also provided.
Lipschitz映射迭代函数系统的鲁棒性
设$\{X_n\}_{n\in{\mathbb{N}}}$为一个${\mathbb{X}}$值的Lipschitz映射迭代函数系统(IFS),定义为$X_0 \in {\mathbb{X}}$和$n\geq 1$, $X_n\;:\!=\;F(X_{n-1},\vartheta_n)$,其中$\{\vartheta_n\}_{n \ge 1}$为具有共同概率分布的独立同分布随机变量,$\mathfrak{p}$, $F(\cdot,\cdot)$在第一个变量上为Lipschitz连续,$X_0$独立于$\{\vartheta_n\}_{n \ge 1}$。在F和$\mathfrak{p}$的参数扰动下,我们感兴趣的是$\{X_n\}_{n\in{\mathbb{N}}}$的v几何遍历性的鲁棒性,它的不变概率测度的鲁棒性,最后是$X_n$的概率分布的鲁棒性。具体来说,我们提出了一种假设模式来研究IFS的这种鲁棒性。该模式适用于具有自回归条件异方差误差的自回归过程,以及舍入误差或阈值/截断下的IFS。此外,我们还提供了一组一般假设,涵盖了经典的feller型假设,以证明IFS是一个v几何遍历过程。并给出了收敛速度的精确界。
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来源期刊
Journal of Applied Probability
Journal of Applied Probability 数学-统计学与概率论
CiteScore
1.50
自引率
10.00%
发文量
92
审稿时长
6-12 weeks
期刊介绍: Journal of Applied Probability is the oldest journal devoted to the publication of research in the field of applied probability. It is an international journal published by the Applied Probability Trust, and it serves as a companion publication to the Advances in Applied Probability. Its wide audience includes leading researchers across the entire spectrum of applied probability, including biosciences applications, operations research, telecommunications, computer science, engineering, epidemiology, financial mathematics, the physical and social sciences, and any field where stochastic modeling is used. A submission to Applied Probability represents a submission that may, at the Editor-in-Chief’s discretion, appear in either the Journal of Applied Probability or the Advances in Applied Probability. Typically, shorter papers appear in the Journal, with longer contributions appearing in the Advances.
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