M. Remedios Sillero-Denamiel

M. Remedios Sillero-Denamiel
Trinity College Dublin | TCD · School of Computer Science and Statistics

PhD in Mathematics
Research Fellow

About

10
Publications
4,494
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64
Citations
Citations since 2016
10 Research Items
63 Citations
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Publications

Publications (10)
Article
Many real-life applications consider nominal categorical predictor variables that have a hierarchical structure, e.g. economic activity data in Official Statistics. In this paper, we focus on linear regression models built in the presence of this type of nominal categorical predictor variables, and study the consolidation of their categories to hav...
Article
The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional independence, and may deteriorate the method’s performance. Moreover, datasets are often characterized by a large number of features,...
Article
Full-text available
The Naïve Bayes is a tractable and efficient approach for statistical classification. In general classification problems, the consequences of misclassifications may be rather different in different classes, making it crucial to control misclassification rates in the most critical and, in many realworld problems, minority cases, possibly at the expe...
Article
The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individuals of interest is allowed. In this work we propose a novel version of the Las...
Article
Full-text available
Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with...
Preprint
Full-text available
Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components , but at the same time it may overfit, it may be distorted by base regressors with...
Preprint
Full-text available
In this paper, we study linear regression models built on categorical predictor variables that have a hierarchical structure. For such variables, the categories are arranged as a directed tree, where the categories in the leaf nodes give the highest granularity in the representation of the variable. Instead of taking the fully detailed model, the u...
Preprint
Full-text available
COVID-19 is an infectious disease that was first identified in China in December 2019. Subsequently COVID-19 started to spread broadly, to also arrive in Spain by the end of Jan-uary 2020. This pandemic triggered confinement measures, in order to reduce the expansion of the virus so as not to saturate the health care system. With the aim of providi...
Article
The aim of this work is to solve identification problems in plug-flow chemical reactors. For this purpose an adjoint-based algorithm for parameter identification problems in systems of partial differential equations is presented. The adjoint method allows us to calculate the gradient of the objective function and the constraint functions with respe...
Article
A fundamental problem in the analysis of chemical reactions networks consists of identifying concentrations values along time or in steady state which are coherent with the experimental concentrations data available. When concentrations measurements are incomplete, either because information is missing about the concentration of a species at a part...

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Project (1)
Project
NeEDS (Network of European Data Scientists) provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics.