Ricerc@Sapienza

Objective: To examine whether and how avoidable emergency department (ED) utilization is associated with ambulatory or primary care (APC) utilization, insurance, and interaction effects. Design and sample: A cross-sectional analysis of electronic health records from 70,870 adults residing in Mecklenburg County, North Carolina, who visited an ED within a large integrated healthcare system in 2017. Methods: APC utilization was measured as total visits, categorized as: 0, 1, and > 1.

Objective
Heavy users of the emergency department (ED) are a heterogeneous population. Few studies have captured the social and demographic complexity of patients with the largest burden of ED use. Our objective was to model associations between social and demographic patient characteristics and quantiles of the distributions of ED use, defined as frequent and high‐charge.
Methods

This work is intended to assess the contribution to systemic risk of major companies
in the European stock market on a geographical basis. We use the EuroStoxx 50
Index as a proxy for the financial system and we rely on the CoVaR and Delta-CoVaR risk
measures to estimate the contribution of each European country belonging to the index to
systemic risk. We also conduct the significance and dominance test to evaluate whether
the systemic relevance of considered countries is statistically significant and to determine

This paper develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The components of the finite mixture are associated

This paper proposes a maximum likelihood approach to jointly estimate marginal conditional quantiles of multivariate response variables in a linear regression framework. We consider a slight reparameterization of the multivariate asymmetric Laplace distribution proposed by Kotz et al. (2001) and exploit its location–scale mixture representation to implement a new EM algorithm for estimating model parameters.

Quantile regression is an efficient tool when it comes to estimate popular measures of tail
risk such as the conditional quantile Value at Risk. In this paper we exploit the availability
of data at mixed frequency to build a volatility model for daily returns with low– (for macro–
variables) and high–frequency (which may include an “–X” term related to realized volatility
measures) components. The quality of the suggested quantile regression model, labeled MF–
Q–ARCH–X, is assessed in a number of directions: we derive weak stationarity properties, we