34 citations · 36 across the 4 of their papers we have counts for
6 papers · 1 filter
Quantile Mixed Hidden Markov Models for multivariate longitudinal data
Luca Merlo, Lea Petrella, Nikos Tzavidis
The identification of factors associated with mental and behavioral disorders in early childhood is critical both for psychopathology research and the support of primary health car…
A two-part finite mixture quantile regression model for semi-continuous longitudinal data
Antonello Maruotti, Luca Merlo, Lea Petrella
This paper develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence…
Joint estimation of conditional quantiles in multivariate linear regression models. An application to financial distress
Lea Petrella, Valentina Raponi
This paper proposes a maximum-likelihood approach to jointly estimate marginal conditional quantiles of multivariate response variables in a linear regression framework. We conside…
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…