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Prates Machado Fabio

Publications and source records attributed to Prates Machado Fabio.

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The Frog Model on $\mathbb Z$ with Random Discrete Weibull Lifetimes and Biased Nearest-Neighbour Random Walks

We study the frog model on $\mathbb Z$ with particle-wise random discrete Weibull lifetimes and biased nearest-neighbour random walks. Each particle has an independent survival parameter $\pi\in(0,1)$. Conditionally on $\pi=p$, its lifetime $\Xi$ satisfies $$ \mathbb P(\Xi\ge k\mid \pi=p)=p^{k^\gamma}, \, k\in\mathbb{N}_0, $$ where $\gamma>0$. The distribution of $\pi$ is assumed to have right-edge density $$ f_\pi(u)\sim (1-u)^{\beta-1} L\left(\frac1{1-u}\right), \, u\uparrow1, $$ where $\beta>0$ and $L:(0,\infty)\to(0,\infty)$ is a slowly varying function at infinity. The main step is to estimate the tail of the maximal displacement of a single particle before death. If $\tau_n$ denotes the time needed by the underlying walk to reach distance $n$, then $$ \mathbb P(D^*\ge n)=\mathbb E[G(\tau_n)], \, G(k):=\mathbb P(\Xi\ge k). $$ Since $$ G(k)\sim \Gamma(\beta)k^{-\gamma\beta}L(k^\gamma), $$ and the biased nearest-neighbour random walk has linear hitting-time scale, the off-critical threshold is $\beta_c=1/\gamma$. If $\beta>\beta_c$ and the initial number of particles per site has finite mean, the model dies out almost surely. If $\beta<\beta_c$ and the initial configuration is not almost surely empty, the model survives with positive probability in the direction of the drift.

math.PR

Convergence of a Critical Multitype Branching Particle System with Mixed Finite and Infinite Mean Lifetimes

We study a critical multitype branching particle system in $\mathbb{R}^N$ with finite type space $\mathcal{K}={1,\dots,K}$. Particles of type $i$ move according to a symmetric $\alpha_i$-stable process and reproduce according to a critical offspring law with irreducible stochastic mean matrix. All lifetime distributions are non-arithmetic. The type-$1$ lifetime distribution has infinite mean and a regularly varying tail $$ 1-F_1(t)\sim c_1t^{-\gamma}, \qquad 0<\gamma<1, $$ whereas the lifetime distributions of types $2,\dots,K$ satisfy the polynomial upper-tail bounds $$ 1-F_i(t)\le C t^{-\eta_i}, \qquad i=2,\dots,K, \qquad \eta_i>1, \qquad \eta:=\min_{2\le i\le K}\eta_i. $$ The offspring mechanism satisfies a $(1+\beta)$-stable-domain regular-variation condition, with $\beta\in(0,1]$. The initial population is a Poisson random measure with intensity $$ \Lambda=\sum_{i=1}^K a_i\,\lambda\otimes\delta_i, \qquad a_i\ge0, $$ and we set $A:=\sum_{i=1}^K a_i$. Under the space--lifetime condition $$ \rho:=\left(\eta-1\right)\wedge\frac{N}{\alpha_1} > \frac{\gamma}{\beta}, $$ we prove that the system converges to a Poisson random measure with intensity $A\lambda\otimes\delta_1$. Thus, although the initial population may assign positive spatial intensity to every type, the limiting population is supported entirely on the infinite-mean type while preserving the aggregate initial spatial intensity $A\lambda$.

math.PR