1 citations · 1 across the 10 of their papers we have counts for
11 papers
Least-Variable Harmonic Matrix-Exponential Distributions
Maria Laura Battagliola, Oscar Peralta
Concentrated matrix-exponential (CME) distributions are random clocks approximating a fixed time, with quality measured by the squared coefficient of variation (SCV); a well-known…
Least Variability in a Polynomial-Square Class of Rational Kernels
Maria Laura Battagliola, Oscar Peralta
We solve an extremal problem for positive randomization kernels that concentrate a random time as tightly as possible around a deterministic target, within the class obtained by ex…
Colored Markov-Modulated Brownian Motion for Preemptive Workload Stacks
Maria Laura Battagliola, Eduardo Medina-Valdés, Oscar Peralta
We develop a colored Markov-modulated Brownian motion model for diffusion-scale systems in which newly created work preempts the active layer, occupies the top of an ordered stack,…
Equivalence and Separation for Multivariate Matrix-Exponential and Phase-Type Distribution Classes
Oscar Peralta
We resolve two questions left open by Bladt and Nielsen (2010) concerning multivariate families of matrix-exponential and phase-type distributions. First, in the matrix-exponential…
Optimization-Free Concentrated Matrix-Exponentials
Maria Laura Battagliola, Oscar Peralta
Near-deterministic positive delays require highly concentrated distributions, but phase-type models are constrained by the Erlang variance limit. While matrix-exponential distribut…
Matrix Representations for Scale Functions of Spectrally Negative Lévy Processes with Rational Jumps
Osvaldo Angtuncio Hernández, Oscar Peralta
For a spectrally negative Lévy process with Laplace transform , the -scale function is characterized as the function whose Laplace transform is . It has ap…
Rational arrival processes with strictly positive densities need not be Markovian
Oscar Peralta
Telek (2022) asked whether a rational arrival process (RAP), specified by matrices and and an initial row vector , with strictly positive joint densities and a u…
Assessing continuous common-shock risk through matrix distributions
Martin Bladt, Oscar Peralta, Jorge Yslas
We introduce a class of continuous-time bivariate phase-type distributions for modeling dependencies from common shocks. The construction uses continuous-time Markov processes that…
Modeling discrete common-shock risks through matrix distributions
Martin Bladt, Eric C. K. Cheung, Oscar Peralta +1
We introduce a novel class of bivariate common-shock discrete phase-type (CDPH) distributions to describe dependencies in loss modeling, with an emphasis on those induced by common…
Approximations of semi-Markov processes and insurance policy valuation
Martin Bladt, Andreea Minca, Oscar Peralta
Inspired by a duration-dependent life insurance model, we consider continuous-time semi-Markov jump processes, initially assumed to have a finite state-space. We develop approximat…
Reinforcement Learning for SBM Graphon Games with Re-Sampling
Peihan Huo, Oscar Peralta, Junyu Guo +2
The Mean-Field approximation is a tractable approach for studying large population dynamics. However, its assumption on homogeneity and universal connections among all agents limit…