Quasi-stationarity for one-dimensional renormalized Brownian motion

TL;DR

This paper investigates the quasi-stationary behavior of a one-dimensional renormalized Brownian motion, revealing different limiting distributions depending on the parameter , and establishes the existence of associated Q-processes and quasi-ergodic distributions.

Contribution

It characterizes the quasi-stationary distributions and limits for a renormalized Brownian motion across different parameter regimes, including the existence of Q-processes.

Findings

For  > 1/2, conditioned law converges to a point mass at zero.

For  = 1/2, conditioned law converges to the quasi-stationary distribution of an Ornstein-Uhlenbeck process.

For  < 1/2, conditioned law converges to the quasi-stationary distribution of a Brownian motion.

Abstract

We are interested in the quasi-stationarity of the time-inhomogeneous Markov process X t = B t (t + 1) $\kappa$ where (B t) t$\ge$0 is a one-dimensional Brownian motion and $\kappa$ $\in$ (0, $\infty$). We first show that the law of X t conditioned not to go out from (--1, 1) until the time t converges weakly towards the Dirac measure $\delta$ 0 when $\kappa$ > 1 2 as t goes to infinity. Then we show that this conditioned probability converges weakly towards the quasi-stationary distribution of an Ornstein-Uhlenbeck process when $\kappa$ = 1 2. Finally, when $\kappa$ < 1 2 , it is shown that the conditioned probability converges towards the quasi-stationary distribution of a Brownian motion. We also prove the existence of a Q-process and a quasi-ergodic distribution for $\kappa$ = 1 2 and $\kappa$ < 1 2 .