most citedSuperdiffusive limits for stochastic kinetics driven by self-similar drifts

2 citations · 2 across the 10 of their papers we have counts for

collaborators

10 papers

math.PR2025

Stability of storage processes with general release rates

Miha Brešar, Aleksandar Mijatović, Nikola Sandrić

This paper quantifies the ergodicity and the rate of decay of the tail of the stationary distribution for a broad class of storage models, encompassing constant, linear, and power-…

math.PR2025

Multivariate CLT for Lévy processes: convergence rates without moment assumptions

Jorge González Cázares, David Kramer-Bang, Aleksandar Mijatović

We prove that the norm of a -dimensional Lévy process possesses a finite second moment if and only if the convex distance between an appropriately rescaled process at time a…

math.PR2025

Critical branching processes with immigration: scaling limits of local extinction sets

Aleksandar Mijatović, Benjamin Povar, Gerónimo Uribe Bravo

We establish the joint scaling limit of a critical Bienaymé-Galton-Watson process with immigration (BGWI) and its (counting) local time at zero to the corresponding self-similar co…

math.PR2024

Central limit theorem for superdiffusive reflected Brownian motion

Aleksandar Mijatović, Isao Sauzedde, Andrew Wade

We study the second-order asymptotics around the superdiffusive strong law~\cite{MMW} of a multidimensional driftless diffusion with oblique reflection from the boundary in a gener…

math.PR2024

Asymptotically optimal Wasserstein couplings for the small-time stable domain of attraction

Jorge González Cázares, David Kramer-Bang, Aleksandar Mijatović

We develop two novel couplings between general pure-jump Lévy processes in and apply them to obtain upper bounds on the rate of convergence in an appropriate Wasserstein dis…

stat.ML2024

Limit Theorems for Stochastic Gradient Descent with Infinite Variance

Jose Blanchet, Aleksandar Mijatović, Wenhao Yang

Stochastic gradient descent is a classic algorithm that has gained great popularity especially in the last decades as the most common approach for training models in machine learni…