8 citations · 9 across the 4 of their papers we have counts for
6 papers
E-ROBOT: a dimension-free method for robust statistics and machine learning via Schrödinger bridge
Davide La Vecchia, Hang Liu
We propose the Entropic-regularized Robust Optimal Transport (E-ROBOT) framework, a novel method that combines the robustness of ROBOT with the computational and statistical benefi…
On the use of the cumulant generating function for inference on time series
Alban Moor, Davide La Vecchia, Elvezio Ronchetti
We introduce innovative inference procedures for analyzing time series data. Our methodology enables density approximation and composite hypothesis testing based on Whittle's estim…
General Spatio-Temporal Factor Models for High-Dimensional Random Fields on a Lattice
Matteo Barigozzi, Davide La Vecchia, Hang Liu
Motivated by the need for analysing large spatio-temporal panel data, we introduce a novel dimensionality reduction methodology for -dimensional random fields observed across a…
Some novel aspects of quantile regression: local stationarity, random forests and optimal transportation
Manon Felix, Davide La Vecchia, Hang Liu +1
This paper is written for a Festschrift in honour of Professor Marc Hallin and it proposes some developments on quantile regression. We connect our investigation to Marc's scientif…
Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach
Marc Hallin, Davide La Vecchia, Hang Liu
We develop a class of tests for semiparametric vector autoregressive (VAR) models with unspecified innovation densities, based on the recent measure-transportation-based concepts o…
Center-Outward R-Estimation for Semiparametric VARMA Models
Marc Hallin, Davide La Vecchia, Hang Liu
We propose a new class of R-estimators for semiparametric VARMA models in which the innovation density plays the role of the nuisance parameter. Our estimators are based on the nov…