175 citations · 251 across the 15 of their papers we have counts for
11 papers · 1 filter
Calibrated quantile prediction for Growth-at-Risk
Pietro Bogani, Matteo Fontana, Luca Neri +1
Accurate computation of robust estimates for extremal quantiles of empirical distributions is an essential task for a wide range of applicative fields, including economic policymak…
Conformal Prediction Sets for Populations of Graphs
Anna Calissano, Matteo Fontana, Gianluca Zeni +1
The analysis of data such as graphs has been gaining increasing attention in the past years. This is justified by the numerous applications in which they appear. Several methods ar…
Bayesian Functional Emulation of CO2 Emissions on Future Climate Change Scenarios
Luca Aiello, Matteo Fontana, Alessandra Guglielmi
We propose a statistical emulator for a climate-economy deterministic integrated assessment model ensemble, based on a functional regression framework. Inference on the unknown par…
Challenges and Opportunities of Computational Social Science for Official Statistics
Serena Signorelli, Matteo Fontana, Lorenzo Gabrielli +1
The vast amount of data produced everyday (so-called 'digital traces') and available nowadays represent a gold mine for the social sciences, especially in a computational context,…
Conformal Prediction Bands for Two-Dimensional Functional Time Series
Niccolò Ajroldi, Jacopo Diquigiovanni, Matteo Fontana +1
Time evolving surfaces can be modeled as two-dimensional Functional time series, exploiting the tools of Functional data analysis. Leveraging this approach, a forecasting framework…
conformalInference.multi and conformalInference.fd: Twin Packages for Conformal Prediction
Paolo Vergottini, Matteo Fontana, Jacopo Diquigiovanni +2
Building on top of a regression model, Conformal Prediction methods produce distribution free prediction sets, requiring only i.i.d. data. While R packages implementing such method…
Distribution-Free Prediction Bands for Multivariate Functional Time Series: an Application to the Italian Gas Market
Jacopo Diquigiovanni, Matteo Fontana, Simone Vantini
Uncertainty quantification in forecasting represents a topic of great importance in energy trading, as understanding the status of the energy market would enable traders to directl…
Conformal Prediction Bands for Multivariate Functional Data
Jacopo Diquigiovanni, Matteo Fontana, Simone Vantini
Motivated by the pressing request of methods able to create prediction sets in a general regression framework for a multivariate functional response and pushed by new methodologica…
The Importance of Being a Band: Finite-Sample Exact Distribution-Free Prediction Sets for Functional Data
Jacopo Diquigiovanni, Matteo Fontana, Simone Vantini
Functional Data Analysis represents a field of growing interest in statistics. Despite several studies have been proposed leading to fundamental results, the problem of obtaining v…
Smooth Lasso Estimator for the Function-on-Function Linear Regression Model
Fabio Centofanti, Matteo Fontana, Antonio Lepore +1
A new estimator, named S-LASSO, is proposed for the coefficient function of the Function-on-Function linear regression model. The S-LASSO estimator is shown to be able to increase…
Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models
Matteo Fontana, Massimo Tavoni, Simone Vantini
Understanding the dynamics and evolution of climate change and associated uncertainties is key for designing robust policy actions. Computer models are key tools in this scientific…