A phase transition for large values of bifurcating autoregressive models

TL;DR

This paper investigates the asymptotic behavior and phase transition phenomena of large values in bifurcating autoregressive processes, linking large deviations in a random environment to population dynamics and trajectory analysis.

Contribution

It introduces the first analysis of large deviations for autoregressive processes in random environments and develops trajectorial estimates for bifurcating autoregressive processes.

Findings

Two regimes identified based on autoregressive parameter values

Different asymptotic behaviors for large local densities

Law of large numbers for non-homogeneous trees established

Abstract

We describe the asymptotic behavior of the number $Z_n[a_n,\infty)$ of individuals with a large value in a stable bifurcating autoregressive process. The study of the associated first moment $\mathbb{E}(Z_n[a_n,\infty))$ is equivalent to the annealed large deviation problem $\mathbb{P}(Y_n\geq a_n)$, where $Y$ is an autoregressive process in a random environment and $a_n\rightarrow \infty$. The population with large values and the trajectorial behavior of $Z_n[a_n,\infty)$ is obtained from the ancestral paths associated to the large deviations of $Y$ together with its environment. The study of large deviations of autoregressive processes in random environment is of independent interest and achieved first in this paper. The proofs of trajectorial estimates for bifurcating autoregressive process involves then a law of large numbers for non-homogenous trees. Two regimes appear in the stable case, depending on the fact that one of the autoregressive parameter is greater than one or not. It yields two different asymptotic behaviors for the large local densities and maximal value of the bifurcating autoregressive process.