ROBUSTNESS OF ITERATED FUNCTION SYSTEMS OF LIPSCHITZ MAPS

被引:0
|
作者
Herve, Loic [1 ]
Ledoux, James [1 ]
机构
[1] Univ Rennes, INSA Rennes, CNRS, IRMAR UMR 6625, Rennes, France
关键词
Markov chain; geometric ergodicity; perturbation; non-linear stochastic recursion; autoregressive process; MAXIMUM-LIKELIHOOD ESTIMATOR; GEOMETRIC-CONVERGENCE RATES; MARKOV-CHAINS; PERTURBATION-THEORY; ERGODICITY; STABILITY; THEOREM; KERNELS;
D O I
10.1017/jpr.2022.107
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Let {X-n}(n is an element of N) be an X-valued iterated function system (IFS) of Lipschitz maps defined as X-0 is an element of X and for n >= 1, X-n := F(Xn-1, (SIC)(n)), where {(SIC)(n)}(n >= 1 )are independent and identically distributed random variables with common probability distribution p, F(middot, middot) is Lipschitz continuous in the first variable, and X0 is independent of {(SIC)(n)}(n >= 1). Under parametric perturbation of both F and p, we are interested in the robustness of the V geometrical ergodicity property of {X-n}(n is an element of 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.
引用
收藏
页码:921 / 944
页数:24
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