Persistence exponents via perturbation theory: AR(1)-processes

TL;DR

This paper investigates the decay rate of persistence probabilities in AR(1)-processes using perturbation theory to expand eigenvalues, providing insights into the exponential decay behavior for normally distributed cases.

Contribution

It introduces a perturbation method to compute series expansions of eigenvalues related to persistence probabilities in AR(1)-processes, advancing understanding of their decay rates.

Findings

Eigenvalue series expansion in parameter  for Gaussian AR(1)-processes

Exponential decay rate of persistence probabilities identified

Perturbation technique applicable to a class of Markov processes

Abstract

For AR(1)-processes $X_n=\rho X_{n-1}+\xi_n$, $n\in\mathbb{N}$, where $\rho\in\mathbb{R}$ and $(\xi_i)_{i\in\mathbb{N}}$ is an i.i.d. sequence of random variables, we study the persistence probabilities $\mathbb{P}(X_0\ge 0,\dots, X_N\ge 0)$ for $N\to\infty$. For a wide class of Markov processes a recent result [Aurzada, Mukherjee, Zeitouni; arXiv:1703.06447; 2017] shows that these probabilities decrease exponentially fast and that the rate of decay can be identified as an eigenvalue of some integral operator. We discuss a perturbation technique to determine a series expansion of the eigenvalue in the parameter $\rho$ for normally distributed AR(1)-processes.