0000000001203383
AUTHOR
L Augugliaro
Cyclic coordinate for penalized Gaussian graphical models with symmetry restriction
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.
DgCox: a differential geometric approach for high-dimensional Cox proportional hazard models
Many clinical and epidemiological studies rely on survival modelling to detect clinically relevant factors that affect various event histories. With the introduction of high-throughput technologies in the clinical and even large-scale epidemiological studies, the need for inference tools that are able to deal with fat data-structures, i.e., relatively small number of observations compared to the number of features, is becoming more prominent. This paper will introduce a principled sparse inference methodology for proportional hazards modelling, based on differential geometrical analyses of the high-dimensional likelihood surface.
Robustness of dynamic gene regulatory networks in Neisseria
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…
Investigating the Heartbeat-evoked cortical responses through parametric Time-Varying Information Measures
Recent studies showed that the information coming from the heart is constantly processed by the brain. One index to study this process is the heartbeat-evoked potential (HEP), represented by an event-related potential component related to the cortical processing of the heartbeat. In this study we propose an approach to investigate the heartbeat-evoked EEG responses, based on quantifying the changes induced by the heartbeat on the predictability of the brain dynamics. The regularity of EEG signals is assessed through the Information Storage (IS) computed with a time-varying approach able to derive the temporal profile of the measure for each time point. Results show a modulation in the regul…
Differential geometric LARS via cyclic coordinate descent method
We address the problem of how to compute the coefficient path implicitly defined by the differential geometric LARS (dgLARS) method in a high-dimensional setting. Although the geometrical theory developed to define the dgLARS method does not need of the definition of a penalty function, we show that it is possible to develop a cyclic coordinate descent algorithm to compute the solution curve in a high-dimensional setting. Simulation studies show that the proposed algorithm is significantly faster than the prediction-corrector algorithm originally developed to compute the dgLARS solution curve.