Matteo Iacopini
Biographic Data
| ID | 8920821 |
|---|---|
| NAME | Matteo Iacopini |
| GIVEN NAMES | Matteo |
| FAMILY NAME | Iacopini |
| SIGNATURE | IACOPINI M |
| AFFILIATIONS | Libera Università Internazionale degli Studi Sociali Guido Carli |
| ORCID | 0000-0002-3551-4891 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Static and Dynamic Bart for Rank-Order Data
Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with l…
Bayesian Dynamic Tensor Regression
High-and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for pro…
Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions
This article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statisticall…
No prominent works on this page.
Bayesian Dynamic Tensor Regression
High-and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for pro…
Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions
This article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statisticall…
Static and Dynamic Bart for Rank-Order Data
Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with l…
Autoregressive model (2 works) · Bayesian probability (2 works) · Computer Science (2 works) · Econometrics (2 works) · Mathematics (2 works) · Advanced Neuroimaging Techniques and Applications (1 work) · Advanced Statistical Methods and Models (1 work) · Algorithm (1 work) · Artificial Intelligence (1 work) · Bayesian inference (1 work)