7 citations · 8 across the 5 of their papers we have counts for
5 papers
A Bayesian time-varying random partition model for large spatio-temporal datasets
Giulio Beltramin, Andrea Cremaschi, Annalisa Cadonna +2
Spatio-temporal areal data can be seen as a collection of time series which are spatially correlated, according to a specific neighbouring structure. Motivated by a dataset on mobi…
Bayesian modeling and clustering for spatio-temporal areal data: An application to Italian unemployment
Alexander Mozdzen, Andrea Cremaschi, Annalisa Cadonna +2
Spatio-temporal areal data can be seen as a collection of time series which are spatially correlated according to a specific neighboring structure. Incorporating the temporal and s…
Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data
Laura Vana, Ennio Visconti, Laura Nenzi +2
We propose an interdisciplinary framework that combines Bayesian predictive inference, a well-established tool in Machine Learning, with Formal Methods rooted in the computer scien…
Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models
Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus
Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the effect of a predictor on the outcome variable. However, in particular when the number of p…
Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP
Peter Knaus, Angela Bitto-Nemling, Annalisa Cadonna +1
Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TV…