Ramón A. Delgado

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In this paper we obtain the maximum likelihood estimate of the parameters of discrete-time linear models by using a dual time–frequency domain approach. We propose a formulation that considers a (reduced-rank) linear transformation of the available data. Such a transformation may correspond to different options: selection of time-domain data, transformation(More)
A fundamental issue in Elastic Optical Networks (EONs) relies on choosing a proper route and necessary number of contiguous frequency slots from end-to-end to accommodate the traffic demands. Spectrum assignment based on the traditional First-Fit assignment has been extensively employed in EON investigations due to its inherent simplicity and favorable(More)
In this paper we develop a novel approach to model error modelling. There are natural links to others recently developed ideas. However, here we make several key departures, namely (i) we focus on relative errors; (ii) we use a broad class of model error description which includes, inter alia, the earlier idea of stochastic embedding; (iii) we estimate(More)
In this paper, we present a general method for rank-constrained optimization. We use an iterative convex optimization procedure where it is possible to include any extra convex constraints. The proposed approach has potential application in several areas. We focus on the problem of Factor Analysis. In this case, our approach provides sufficient flexibility(More)
In this paper we address the problem of estimating a sparse parameter vector that defines a logistic regression. The problem is then solved using two approaches: i) inequality constrained Maximum Likelihood estimation and ii) penalized Maximum Likelihood which is closely related to Information Criteria such as AIC. For the promotion of sparsity, we utilize(More)
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