arXiv subjects

Rosalba Radice

Publications and source records attributed to Rosalba Radice.

6 recordsLinked to original sources

arXiv2024

A Bivariate Transformation Model for Time-to-Event Data Affected by Unobserved Confounding: Revisiting the Illinois Reemployment Bonus Experiment

Motivated by empirical studies investigating treatment effects in survival analysis, we propose a bivariate transformation model to quantify the impact of a binary treatment on a time-to-event outcome. The model equations are connected through a bivariate Gaussian distribution, with the dependence parameter capturing unobserved confounding, and are specified as functions of additive predictors to flexibly account for the impacts of observed confounders. Moreover, the baseline survival function is estimated using monotonic P-splines, the effects of binary or factor instruments can be regularized through a ridge penalty approach, and interactions between treatment and observed confounders can be incorporated to accommodate potential variations in treatment effects across subgroups. The proposal naturally provides the survival average treatment effect. Parameter estimation is achieved via an efficient and stable penalized maximum likelihood estimation approach, and intervals constructed using related inferential results. We revisit a dataset from the Illinois Reemployment Bonus Experiment to estimate the effect of a cash bonus on the probability of remaining unemployed at several time points, unveiling interesting insights. The modeling framework is incorporated into the R package GJRM, enabling researchers and practitioners to employ the proposed model and ensuring the reproducibility of results.

stat.ME↗

arXiv2023

Spline-Based Multi-State Models for Analyzing Disease Progression

Motivated by disease progression-related studies, we propose an estimation method for fitting general non-homogeneous multi-state Markov models. The proposal can handle many types of multi-state processes, with several states and various combinations of observation schemes (e.g., intermittent, exactly observed, censored), and allows for the transition intensities to be flexibly modelled through additive (spline-based) predictors. The algorithm is based on a computationally efficient and stable penalized maximum likelihood estimation approach which exploits the information provided by the analytical Hessian matrix of the model log-likelihood. The proposed modeling framework is employed in case studies that aim at modeling the onset of cardiac allograft vasculopathy, and cognitive decline due to aging, where novel patterns are uncovered. To support applicability and reproducibility, all developed tools are implemented in the R package flexmsm.

stat.ME↗

arXiv2020

Modelling the Extremes of Seasonal Viruses and Hospital Congestion: The Example of Flu in a Swiss Hospital

Viruses causing flu or milder coronavirus colds are often referred to as "seasonal viruses" as they tend to subside in warmer months. In other words, meteorological conditions tend to impact the activity of viruses, and this information can be exploited for the operational management of hospitals. In this study, we use three years of daily data from one of the biggest hospitals in Switzerland and focus on modelling the extremes of hospital visits from patients showing flu-like symptoms and the number of positive cases of flu. We propose employing a discrete Generalized Pareto distribution for the number of positive and negative cases, and a Generalized Pareto distribution for the odds of positive cases. Our modelling framework allows for the parameters of these distributions to be linked to covariate effects, and for outlying observations to be dealt with via a robust estimation approach. Because meteorological conditions may vary over time, we use meteorological and not calendar variations to explain hospital charge extremes, and our empirical findings highlight their significance. We propose a measure of hospital congestion and a related tool to estimate the resulting CaRe (Charge-at-Risk-estimation) under different meteorological conditions. The relevant numerical computations can be easily carried out using the freely available GJRM R package. The introduced approach could be applied to several types of seasonal disease data such as those derived from the new virus SARS-CoV-2 and its COVID-19 disease which is at the moment wreaking havoc worldwide. The empirical effectiveness of the proposed method is assessed through a simulation study.

stat.ME↗

arXiv2019

Robust Fitting for Generalized Additive Models for Location, Scale and Shape

The validity of estimation and smoothing parameter selection for the wide class of generalized additive models for location, scale and shape (GAMLSS) relies on the correct specification of a likelihood function. Deviations from such assumption are known to mislead any likelihood-based inference and can hinder penalization schemes meant to ensure some degree of smoothness for non-linear effects. We propose a general approach to achieve robustness in fitting GAMLSSs by limiting the contribution of observations with low log-likelihood values. Robust selection of the smoothing parameters can be carried out either by minimizing information criteria that naturally arise from the robustified likelihood or via an extended Fellner-Schall method. The latter allows for automatic smoothing parameter selection and is particularly advantageous in applications with multiple smoothing parameters. We also address the challenge of tuning robust estimators for models with non-linear effects by proposing a novel median downweighting proportion criterion. This enables a fair comparison with existing robust estimators for the special case of generalized additive models, where our estimator competes favorably. The overall good performance of our proposal is illustrated by further simulations in the GAMLSS setting and by an application to functional magnetic resonance brain imaging using bivariate smoothing splines.

stat.ME↗

arXiv2019

Generalised Joint Regression for Count Data with a Focus on Modelling Football Matches

We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and non-linear dependence through the use of several copulae. Moreover, the parameters of the marginal distributions of the count responses and of the copula can be specified as flexible functions of covariates. Motivated by a football application, we also discuss an extension which forces the regression coefficients of the marginal (linear) predictors to be equal via a suitable penalisation. Model fitting is based on a trust region algorithm which estimates simultaneously all the parameters of the joint models. We investigate the proposal's empirical performance in two simulation studies, the first one designed for arbitrary count data, the other one reflecting football-specific settings. Finally, the method is applied to FIFA World Cup data, showing its competitiveness to the standard approach with regard to predictive performance.

stat.AP↗

arXiv2016

A Bivariate Copula Additive Model for Location, Scale and Shape

Rigby & Stasinopoulos (2005) introduced generalized additive models for location, scale and shape (GAMLSS) where the response distribution is not restricted to belong to the exponential family and its parameters can be specified as functions of additive predictors that allows for several types of covariate effects (e.g., linear, non-linear, random and spatial effects). In many empirical situations, however, modeling simultaneously two or more responses conditional on some covariates can be of considerable relevance. In this article, we extend the scope of GAMLSS by introducing a bivariate copula additive model with continuous margins for location, scale and shape. The framework permits the copula dependence and marginal distribution parameters to be estimated simultaneously and, like in GAMLSS, each parameter to be modeled using an additive predictor. Parameter estimation is achieved within a penalized likelihood framework using a trust region algorithm with integrated automatic multiple smoothing parameter selection. The proposed approach allows for straightforward inclusion of potentially any parametric continuous marginal distribution and copula function. The models can be easily used via the copulaReg() function in the R package SemiParBIVProbit. The usefulness of the proposal is illustrated on two case studies (which use electricity price and demand data, and birth records) and on simulated data.

stat.ME↗