0000000001062531
AUTHOR
A Abbruzzo
showing 4 related works from this author
Model selection for penalized Gaussian Graphical Models
2013
High-dimensional data refers to the case in which the number of parameters is of one or more order greater than the sample size. Penalized Gaussian graphical models can be used to estimate the conditional independence graph in high-dimensional setting. In this setting, the crucial issue is to select the tuning parameter which regulates the sparsity of the graph. In this paper, we focus on estimating the "best" tuning parameter. We propose to select this tuning parameter by minimizing an information criterion based on the generalized information criterion and to use a stability selection approach in order to obtain a more stable graph. The performance of our method is compared with the state…
Interregional mobility, socio-economic inequality and mortality among cancer patients
2020
This paper investigates 3-years mortality after discharge in patients residing in Sicily (Italy) diagnosed with cancer among: colon, stomach, liver, and lungs, between 1/1/2010 - 31/12/2011. The effect of mobility and socio-economic status on mortality is evaluated through survival analysis approach. Results shows that out-of-region hospitalization is associated with higher survival time; no association of mortality with socio-economic status appears. The extent of patients’ mobility, and its relation with mortality raises regional policy considerations
Cyclic coordinate for penalized Gaussian graphical models with symmetry restriction
2014
In this paper we propose two efficient cyclic coordinate algorithms to estimate structured concentration matrix in penalized Gaussian graphical models. Symmetry restrictions on the concentration matrix are particularly useful to reduce the number of parameters to be estimated and to create specific structured graphs. The penalized Gaussian graphical models are suitable for high-dimensional data.
Robustness of dynamic gene regulatory networks in Neisseria
2014
Gene regulatory networks are made of highly tuned, sparse and dynamical operations. We consider the case of the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis, and aim to infer a robust net- work of interactions across sixty proteins based on a detailed time course gene expres- sion study. We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood under a structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The authors developed a new optimization algo…