L
Luca Rossini
Researcher at Tuscia University
Publications - 75
Citations - 495
Luca Rossini is an academic researcher from Tuscia University. The author has contributed to research in topics: Bayesian probability & Prior probability. The author has an hindex of 9, co-authored 57 publications receiving 285 citations. Previous affiliations of Luca Rossini include Ca' Foscari University of Venice & University of London.
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Bayesian nonparametric sparse VAR models
TL;DR: In this paper, a hierarchical BNP-Lasso prior is proposed for high-dimensional vector autoregressive (VAR) models, which can improve estimation efficiency and prediction accuracy by clustering VAR coefficients into groups and shrinking the coefficients of each group toward a common location.
Journal ArticleDOI
Comparing the forecasting performances of linear models for electricity prices with high RES penetration
Angelica Gianfreda,Angelica Gianfreda,Francesco Ravazzolo,Francesco Ravazzolo,Luca Rossini,Luca Rossini +5 more
TL;DR: In this paper, the authors compare alternative univariate versus multivariate models and frequentist versus Bayesian autoregressive and vector autoregression specifications for hourly day-ahead electricity prices, both with and without renewable energy sources.
Posted Content
Bayesian nonparametric sparse VAR models
TL;DR: This work proposes a new Bayesian nonparametric (BNP) Lasso prior for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy and overcomes overparametrization and overfitting issues.
Posted Content
Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration
Angelica Gianfreda,Angelica Gianfreda,Francesco Ravazzolo,Francesco Ravazzolo,Luca Rossini,Luca Rossini +5 more
TL;DR: In this article, the authors compared alternative univariate versus multivariate models, frequentist versus Bayesian autoregressive and vector autoregression specifications, for hourly day-ahead electricity prices, both with and without renewable energy sources.
Journal ArticleDOI
Bayesian non-parametric conditional copula estimation of twin data
TL;DR: The purpose is to analyse correctly the influence of socio‐economic status on the relationship between twins’ cognitive abilities and proposes a flexible Bayesian non‐parametric approach for the estimation of conditional copulas, which can model any conditional copula density.