2013
Autores
Adelaide Figueiredo; Paulo Gomes;
Publicação
Abstract
2015
Autores
Adelaide Figueiredo; Fernanda Figueiredo;
Publicação
Abstract
2018
Autores
Adelaide Figueiredo; Fernanda Figueiredo;
Publicação
Abstract
2017
Autores
Fernanda Figueiredo; Adelaide Figueiredo; Alexandra Ramos; Paulo Teles;
Publicação
Abstract
2024
Autores
Figueiredo, AMS; Figueiredo, FO;
Publicação
Research in Statistics
Abstract
Abstract.: We consider the headline indicators of the Europe 2020 agenda for the European Union countries for several years of the period 2010–2019 and their own national targets for these indicators. The indicators belong to five thematic areas: employment; education; research, development, and innovation; poverty and social exclusion; climate change; and energy. The main objective of this article is to analyze the dynamics and evolution of the EU countries and the Agenda Europe 2020 indicators over the period, taking into account the relations between the indicators for the EU countries along the years. In order to analyze the different data tables, we have used a three-way data methodology, the STATIS methodology. The results obtained show that the countries of the European Union as a whole have made progress towards the global targets set for the different indicators, with some countries making more significant progress than others. The indicators related to research, development, and innovation, as well as climate change and energy, are the ones where the most improvement is needed. The targets set individually for each country, less demanding for some and more daring for others, were generally already achieved in 2019 or are very close to being achieved. © 2025 Elsevier B.V., All rights reserved.
2014
Autores
Figueiredo A.; Figueiredo F.;
Publicação
Proceedings of COMPSTAT 2014 - 21st International Conference on Computational Statistics
Abstract
In real situations the evaluation of the global quality of either a product or a service depends on more than one quality characteristic. In order to monitor the variability of multivariate processes and identify the variables responsible for changes in the process, we will use the STATIS (Structuration des Tableaux A Trois Indices de la Statistique) methodology, a three-way data analysis method. For this purpose we consider a control chart based on a similarity measure between two positive semi-definite matrices, the RV coefficient, and we evaluate the performance of this control chart for monitoring multivariate normal data.
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.