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Brain-computer interfaces (BCI) are presented as a solution for people with global paralysis, also known as locked-in syndrome (LIS). The targeted population includes the most severe patients, with no residual eye movements, who cannot use any commun
Externí odkaz:
http://arxiv.org/abs/2310.00266
Autor:
Bernardi, Enrico, Farnè, Matteo
This paper provides a comprehensive estimation framework for large covariance matrices via a log-det heuristics augmented by a nuclear norm plus $l_{1}$ norm penalty. %We develop the model framework, which includes high-dimensional approximate factor
Externí odkaz:
http://arxiv.org/abs/2209.04867
Publikováno v:
In Cortex October 2024 179:235-246
Autor:
Roberto Farné
Publikováno v:
Encyclopaideia, Vol 28, Iss 68, Pp i-ii (2024)
Externí odkaz:
https://doaj.org/article/bd283915ad694246a5125c48344bdf8e
Autor:
Farnè, Matteo, Vouldis, Angelos
Publikováno v:
In The Journal of Economic Asymmetries June 2024 29
Autor:
Farnè, Marianna, Fortunato, Fernanda, Neri, Marcella, Farnè, Matteo, Balla, Cristina, Albamonte, Emilio, Barp, Andrea, Armaroli, Annarita, Perugini, Enrica, Carinci, Valeria, Facchini, Marco, Chiarini, Luca, Sansone, Valeria A., Straudi, Sofia, Tugnoli, Valeria, Sette, Elisabetta, Sensi, Mariachiara, Bertini, Matteo, Evangelista, Teresinha, Ferlini, Alessandra, Gualandi, Francesca
Publikováno v:
In European Journal of Medical Genetics June 2023 66(6)
Autor:
Farnè, Matteo, Vouldis, Angelos
This paper presents a fast methodology, called ROBOUT, to identify outliers in a response variable conditional on a set of linearly related predictors, retrieved from a large granular dataset. ROBOUT is shown to be effective and particularly versatil
Externí odkaz:
http://arxiv.org/abs/2104.12208
Autor:
Farnè, Matteo, Montanari, Angela
This paper provides a comprehensive estimation framework via nuclear norm plus $l_1$ norm penalization for high-dimensional approximate factor models with a sparse residual covariance. The underlying assumptions allow for non-pervasive latent eigenva
Externí odkaz:
http://arxiv.org/abs/2104.02422
Autor:
Barigozzi, Matteo, Farnè, Matteo
We propose a new estimator of high-dimensional spectral density matrices, called UNshrunk ALgebraic Spectral Estimator (UNALSE), under the assumption of an underlying low rank plus sparse structure, as typically assumed in dynamic factor models. The
Externí odkaz:
http://arxiv.org/abs/2104.01863
Publikováno v:
In iScience 15 March 2024 27(3)