Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Luciani, A"'
All modern distributed systems list performance and scalability as their core strengths. Given that optimal performance requires carefully selecting configuration options, and typical cluster sizes can range anywhere from 2 to 300 nodes, it is rare f
Externí odkaz:
http://arxiv.org/abs/2110.05543
Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we p
Externí odkaz:
http://arxiv.org/abs/2004.02706
Autor:
Barigozzi, Matteo, Luciani, Matteo
This paper considers estimation of large dynamic factor models with common and idiosyncratic trends by means of the Expectation Maximization algorithm, implemented jointly with the Kalman smoother. We show that, as the cross-sectional dimension $n$ a
Externí odkaz:
http://arxiv.org/abs/1910.09841
Autor:
Barigozzi, Matteo, Luciani, Matteo
This paper studies Quasi Maximum Likelihood estimation of Dynamic Factor Models for large panels of time series. Specifically, we consider the case in which the autocorrelation of the factors is explicitly accounted for, and therefore the model has a
Externí odkaz:
http://arxiv.org/abs/1910.03821
Autor:
Wentzel, A., Hanula, P., Luciani, T., Elgohari, B., Elhalawani, H., Canahuate, G., Vock, D., Fuller, C. D., Marai, G. E.
We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of trea
Externí odkaz:
http://arxiv.org/abs/1907.05919
Autor:
Barigozzi, Matteo, Luciani, Matteo
This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation
Externí odkaz:
http://arxiv.org/abs/1709.01445
Publikováno v:
Biometrical Journal, 60(1), 174-195, January 2018
In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented. This paper is a draft version of the article published after peer review in 2018 (https://doi.org/10.1002/bimj.201600206). A p
Externí odkaz:
http://arxiv.org/abs/1609.06864
We study a large-dimensional Dynamic Factor Model where: (i)~the vector of factors $\mathbf F_t$ is $I(1)$ and driven by a number of shocks that is smaller than the dimension of $\mathbf F_t$; and, (ii)~the idiosyncratic components are either $I(1)$
Externí odkaz:
http://arxiv.org/abs/1602.02398
The paper studies Non-Stationary Dynamic Factor Models such that the factors $\mathbf F_t$ are $I(1)$ and singular, i.e. $\mathbf F_t$ has dimension $r$ and is driven by a $q$-dimensional white noise, the common shocks, with $q
Externí odkaz:
http://arxiv.org/abs/1510.02399
Autor:
Luciani, Annie, Evrard, Matthieu, Couroussé, Damien, Castagné, Nicolas, Cadoz, Claude, Florens, Jean-Loup
The question of encoding movements such as those produced by human gestures may become central in the coming years, given the growing importance of movement data exchanges between heterogeneous systems and applications (musical applications, 3D motio
Externí odkaz:
http://arxiv.org/abs/1005.4564