46 citations · 56 across the 11 of their papers we have counts for
11 papers
Globally aligned Principal Component Analysis for multi-group data
Hedayat Fathi, Marzia A. Cremona, Federico Severino
We propose a novel principal component analysis (PCA) for multi-group datasets, where the same numerical variables are measured across different groups of observations. Existing ap…
Global testing of SNP-methylation interactions on binary phenotypes via a logistic functional regression model
Yvelin Gansou, Karim Oualkacha, Marzia Angela Cremona +1
Understanding how genetic and epigenetic factors jointly influence binary health outcomes remains a major challenge in biomedical research. We propose a global test for the overall…
Selection of functional predictors and smooth coefficient estimation for scalar-on-function regression models
Hedayat Fathi, Marzia A. Cremona, Federico Severino
In the framework of scalar-on-function regression models, in which several functional variables are employed to predict a scalar response, we propose a methodology for selecting re…
An efficient approach to characterize spatio-temporal dependence in cortical surface fMRI data
Huy Dang, Marzia Cremona, Nicole Lazar +1
Functional magnetic resonance imaging (fMRI) is a neuroimaging technique known for its ability to capture brain activity non-invasively and at fine spatial resolution (2-3mm). Cort…
Contrasting pre-vaccine COVID-19 waves in Italy through Functional Data Analysis
Tobia Boschi, Jacopo Di Iorio, Lorenzo Testa +2
We use data from 107 Italian provinces to characterize and compare mortality patterns in the first two COVID-19 epidemic waves, which occurred prior to the introduction of vaccines…
Analyzing Taiwanese traffic patterns on consecutive holidays through forecast reconciliation and prediction-based anomaly detection techniques
Mahsa Ashouri, Frederick Kin Hing Phoa, Marzia A. Cremona
This study explores traffic patterns on Taiwanese highways during consecutive holidays and focuses on understanding Taiwanese highway traffic behavior. We propose a prediction-base…
funBIalign: a hierachical algorithm for functional motif discovery based on mean squared residue scores
Jacopo Di Iorio, Marzia A. Cremona, Francesca Chiaromonte
Motif discovery is gaining increasing attention in the domain of functional data analysis. Functional motifs are typical "shapes" or "patterns" that recur multiple times in differe…
smoothEM: a new approach for the simultaneous assessment of smooth patterns and spikes
Huy Dang, Marzia Cremona, Francesca Chiaromonte
We consider functional data where an underlying smooth curve is composed not just with errors, but also with irregular spikes. We propose an approach that, combining regularized sp…
The shapes of an epidemic: using Functional Data Analysis to characterize COVID-19 in Italy
Tobia Boschi, Jacopo Di Iorio, Lorenzo Testa +2
We investigate patterns of COVID-19 mortality across 20 Italian regions and their association with mobility, positivity, and socio-demographic, infrastructural and environmental co…
On the bias of H-scores for comparing biclusters, and how to correct it
Jacopo Di Iorio, Francesca Chiaromonte, Marzia A. Cremona
In the last two decades several biclustering methods have been developed as new unsupervised learning techniques to simultaneously cluster rows and columns of a data matrix. These…
Probabilistic -mean with local alignment for clustering and motif discovery in functional data
Marzia A. Cremona, Francesca Chiaromonte
We develop a new method to locally cluster curves and discover functional motifs, i.e.~typical ``shapes'' that may recur several times along and across the curves capturing importa…