16 citations · 25 across the 8 of their papers we have counts for
8 papers
Sign-Flipping Score Tests for Fixed Effects in Multivariate Clustered Data
Angela Andreella, Livio Finos
Standard parametric inference on fixed effects in multivariate linear mixed models can be sensitive to covariance misspecification, particularly with multivariate responses. We pro…
Towards a power analysis for PLS-based methods
Angela Andreella, Livio Fino, Bruno Scarpa +1
In recent years, power analysis has become widely used in applied sciences, with the increasing importance of the replicability issue. When distribution-free methods, such as Parti…
Robust Inference for Generalized Linear Mixed Models: An Approach Based on Score Sign Flipping
Angela Andreella, Jelle Goeman, Jesse Hemerik +1
Despite the versatility of generalized linear mixed models in handling complex experimental designs, they often suffer from misspecification and convergence problems. This makes in…
Procrustes-based distances for exploring between-matrices similarity
Angela Andreella, Riccardo De Santis, Anna Vesely +1
The statistical shape analysis called Procrustes analysis minimizes the distance between matrices by similarity transformations. The method returns a set of optimal orthogonal matr…
Enhanced hyperalignment via spatial prior information
Angela Andreella, Livio Finos, Martin A Lindquist
Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, ev…
A predictive model for planning emergency events rescue during COVID-19 in Lombardy, Italy
Angela Andreella, Antonietta Mira, Spyros Balafas +5
Italy, particularly the Lombardy region, was among the first countries outside of Asia to report cases of COVID-19. The emergency medical service called Regional Emergency Agency (…
Permutation-based true discovery proportions for functional Magnetic Resonance Imaging cluster analysis
Angela Andreella, Jesse Hemerik, Wouter Weeda +2
We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any select…
Procrustes analysis for high-dimensional data
Angela Andreella, Livio Finos
The Procrustes-based perturbation model (Goodall, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from non-id…