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20212026
most citedExtremal Laws for Laplacian Random Matrices

1 citations · 2 across the 7 of their papers we have counts for

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7 papers

math.ST2026

Distributional Convergence of Empirical Entropic Optimal Transport and Statistical Applications

Santiago Arenas-Velilla, Axel Munk, Luis-Alberto Rodríguez

Recently, the statistical properties of empirical Entropic Optimal Transport (EOT) have attracted great interest, as this quantity has been shown to be useful for complex data anal…

math.PR2023

Central limit theorem for crossings in randomly embedded graphs

Santiago Arenas-Velilla, Octavio Arizmendi, J. E. Paguyo

We consider the number of crossings in a random embedding of a graph, , with vertices in convex position. We give explicit formulas for the mean and variance of the number of cr…

math.PR2022

On the links between Stein transforms and concentration inequalities for dependent random variables

Santiago Arenas-Velilla, Emilien Joly

In this paper, we explore some links between transforms derived by Stein's method and concentration inequalities. In particular, we show that the stochastic domination of the zero…

math.PR2022★ 1 cited

Extreme eigenvalues of Laplacian random matrices with Gaussian entries

Andrew Campbell, Kyle Luh, Sean O'Rourke +2

A Laplacian matrix is a real symmetric matrix whose row and column sums are zero. We investigate the limiting distribution of the largest eigenvalues of a Laplacian random matrix w…

math.PR2022

Convergence Rate for The Number of Crossing in a Random Labelled Tree

Santiago Arenas-Velilla, Octavio Arizmendi

We consider the number of crossings in a random labelled tree with vertices in convex position. We give a new proof of the fact that this quantity is asymptotically Gaussian with m…

math.CO2022

Crossings in Randomly Embedded Graphs

Santiago Arenas-Velilla, Octavio Arizmendi

We consider the number of crossings in a graph which is embedded randomly on a convex set of points. We give an estimate to the normal distribution in Kolmogorov distance which imp…