7 papers
Parsimonious Hidden Markov Models for Matrix-Variate Longitudinal Data
Salvatore D. Tomarchio, Antonio Punzo, Antonello Maruotti
Hidden Markov models (HMMs) have been extensively used in the univariate and multivariate literature. However, there has been an increased interest in the analysis of matrix-variat…
Spatial modelling of COVID-19 incident cases using Richards' curve: an application to the Italian regions
Marco Mingione, Pierfrancesco Alaimo Di Loro, Alessio Farcomeni +4
We introduce an extended generalised logistic growth model for discrete outcomes, in which a network structure can be specified to deal with spatial dependence and time dependence…
Nowcasting COVID-19 incidence indicators during the Italian first outbreak
Pierfrancesco Alaimo Di Loro, Fabio Divino, Alessio Farcomeni +4
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecas…
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…
An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions
Alessio Farcomeni, Antonello Maruotti, Fabio Divino +2
The availability of intensive care beds during the Covid-19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simpl…
A copula-based multivariate hidden Markov model for modelling momentum in football
Marius Ötting, Roland Langrock, Antonello Maruotti
We investigate the potential occurrence of change points - commonly referred to as "momentum shifts" - in the dynamics of football matches. For that purpose, we model minute-by-min…