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Alejandro Rosales-Ortiz

Publications and source records attributed to Alejandro Rosales-Ortiz.

5 recordsLinked to original sources

Local times and excursions for self-similar Markov trees

This work builds upon the recent monograph [5] on self-similar Markov trees. A self-similar Markov tree is a random real tree equipped with a function from the tree to $[0,\infty)$ that we call the decoration. Here, we construct local time measures $L(x,dt)$ at every level $x>0$ of the decoration for a large class of self-similar Markov trees. This enables us to mark at random a typical point in the tree at which the decoration is $x$. We identify the law of the decoration along the branch from the root to this tagged point in terms of a remarkable (positive) self-similar Markov process. We also show that after a proper normalization, $L(x,dt)$ converges as $x\to 0+$ to the harmonic measure $μ$ on the tree. Finally, we point out that using a local time measure instead of the usual length measure $λ$ to compute distances on the tree turn the latter into a continuous branching tree. This is relevant to analyze the excusions of the decoration away from a given level. Many results of the present work shall be compared with the recent ones in [22,23] about local times and excursions of a Markov process indexed by Lévy tree.

math.PR↗

Excursion theory for Markov processes indexed by Levy trees

We develop an excursion theory that describes the evolution of a Markov process indexed by a Levy tree away from a regular and instantaneous point $x$ of the state space. The theory builds upon a notion of local time at $x$ that was recently introduced in [37]. Despite the radically different setting, our results exhibit striking similarities to the classical excursion theory for $\mathbb{R}_+$-indexed Markov processes. We then show that the genealogy of the excursions can be encoded in a Levy tree called the tree coded by the local time. In particular, we recover by different methods the excursion theory of Abraham and Le Gall [2], which was developed for Brownian motion indexed by the Brownian tree.

math.PR↗

Noise Reinforced Lévy Processes: Lévy-Itô Decomposition and Applications

A step reinforced random walk is a discrete time process with memory such that at each time step, with fixed probability $p \in (0,1)$, it repeats a previously performed step chosen uniformly at random while with complementary probability $1-p$, it performs an independent step with fixed law. In the continuum, the main result of Bertoin in [7] states that the random walk constructed from the discrete-time skeleton of a Lévy process for a time partition of mesh-size $1/n$ converges, as $n \uparrow \infty$ in the sense of finite dimensional distributions, to a process $\hatξ$ referred to as a noise reinforced Lévy process. Our first main result states that a noise reinforced Lévy processes has rcll paths and satisfies a $\textit{noise reinforced}$ Lévy Itô decomposition in terms of the $\textit{noise reinforced}$ Poisson point process of its jumps. We introduce the joint distribution of a Lévy process and its reinforced version $(ξ, \hatξ)$ and show that the pair, conformed by the skeleton of the Lévy process and its step reinforced version, converge towards $(ξ, \hatξ)$ as the mesh size tend to $0$. As an application, we analyse the rate of growth of $\hatξ$ at the origin and identify its main features as an infinitely divisible process.

math.PR↗

The structure of the local time of Markov processes indexed by Levy trees

We construct the analogue of the local time -- at a fixed point $x$ -- for Markov processes indexed by Levy trees. We start by proving that Markov processes indexed by Levy trees satisfy a special Markov property which can be thought as a spatial version of the classical Markov property. Then, we construct the analogue of the local time by an approximation procedure and we characterize the support of its Lebesgue-Stieltjes measure. We also give an equivalent construction in terms of a special family of exit local times. Finally, combining these results, we show that the points at which the Markov process takes the value $x$ encode a new Levy tree and we construct explicitly its height process. In particular, we recover a recent result of Le Gall concerning the subordinate tree of the Brownian tree where the subordination function is given by the past maximum process of Brownian motion indexed by the Brownian tree.

math.PR↗

Joint invariance principles for random walks with positively and negatively reinforced steps

Given a random walk $(S_n)$ with typical step distributed according to some fixed law and a fixed parameter $p \in (0,1)$, the associated positively step-reinforced random walk is a discrete-time process which performs at each step, with probability $1-p$, the same step as $(S_n)$ while with probability $p$, it repeats one of the steps it performed previously chosen uniformly at random. The negatively step-reinforced random walk follows the same dynamics but when a step is repeated its sign is also changed. In this work, we shall prove functional limit theorems for the triplet of a random walk, coupled with its positive and negative reinforced versions when $p<1/2$ and when the typical step is centred. As our work will show, the limiting process is Gaussian and admits a simple representation in terms of stochastic integrals. Our method exhausts a martingale approach in conjunction with the martingale functional CLT.

math.PR↗