2 citations · 4 across the 10 of their papers we have counts for
12 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,…
Local Second-Order Geometry Induced by Deformation Maps
Maria Laura Battagliola
Spatial deformations offer a flexible route to nonstationary dependence by warping the coordinates of a stationary random field. While the exact induced covariance depends on the d…
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…
Functional Modeling of Learning and Memory Dynamics in Cognitive Disorders
Maria Laura Battagliola, Laura J. Benoit, Sarah Canetta +2
Deficits in working memory, which includes both the ability to learn and to retain information short-term, are a hallmark of many cognitive disorders. Our study analyzes data from…
Estimating Velocity Vector Fields of Atmospheric Winds using Transport Gaussian Processes
Youssef Fahmy, Maria Laura Battagliola, Joseph Guinness
Accurately estimating latent velocity vector fields of atmospheric winds is crucial for understanding weather phenomena. Direct measurement of atmospheric winds is costly, especial…
Localized Functional Principal Component Analysis Based on Covariance Structure
Maria Laura Battagliola, Jan O. Bauer
Functional principal component analysis (FPCA) is a widely used technique in functional data analysis for identifying the primary sources of variation in a sample of random curves.…
Extremile scalar-on-function regression
Maria Laura Battagliola, Martin Bladt
Extremiles provide a generalization of quantiles which are not only robust, but also have an intrinsic link with extreme value theory. This paper introduces an extremile regression…
Quantile regression for longitudinal functional data with application to feed intake of lactating sows
Maria Laura Battagliola, Helle Sørensen, Anders Tolver +1
This article focuses on the study of lactating sows, where the main interest is the influence of temperature, measured throughout the day, on the lower quantiles of the daily feed…
Modeling Spatio-Temporal Transport: From Rigid Advection to Realistic Dynamics
Maria Laura Battagliola, Sofia Charlotta Olhede
Stochastic models for spatio-temporal transport face a critical trade-off between physical realism and interpretability. The advection model with a single constant velocity is inte…
A bias-adjusted estimator in quantile regression for clustered data
Maria Laura Battagliola, Helle Sørensen, Anders Tolver +1
The manuscript discusses how to incorporate random effects for quantile regression models for clustered data with focus on settings with many but small clusters. The paper has thre…