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Applications on inference of biological networks have raised a strong interest in the problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data, and then selects one graph among this family according to a dedicated(More)
In most current data modelling for time-dynamic systems, one works with a pre-specified differential equation and attempts to fit its parameters. In contrast, we demonstrate that in the case of functional data, the equation itself can be inferred from the data. Assuming only that the dynamics are described by a first order nonlinear differential equation(More)
We study the nonparametric covariance estimation of a stationary Gaussian field X observed on a lattice. To tackle this issue, a neighborhood selection procedure has been recently introduced. This procedure amounts to selecting a neighborhood m by a penalization method and estimating the covariance of X in the space of Gaussian Markov random fields (GMRFs)(More)
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