2 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Locally Sparse Function on function Regression
Mauro Bernardi, Antonio Canale, Marco Stefanucci
In functional data analysis, functional linear regression has attracted significant attention recently. Herein, we consider the case where both the response and covariates are func…
The Sparse Multivariate Method of Simulated Quantiles
Mauro Bernardi, Lea Petrella, Paola Stolfi
In this paper the method of simulated quantiles (MSQ) of Dominicy and Veredas (2013) and Dominick et al. (2013) is extended to a general multivariate framework (MMSQ) and to provid…
On the Lp-quantiles for the Student t distribution
Mauro Bernardi, Valeria Bignozzi, Lea Petrella
L_p-quantiles represent an important class of generalised quantiles and are defined as the minimisers of an expected asymmetric power function, see Chen (1996). For p=1 and p=2 the…
Bayesian Robust Quantile Regression
Mauro Bernardi, Marco Bottone, Lea Petrella
Traditional Bayesian quantile regression relies on the Asymmetric Laplace distribution (ALD) mainly because of its satisfactory empirical and theoretical performances. However, the…