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About

About

I am Associate Professor at the School of Economics of the University of Porto, where  I teach Statistics and Multivariate Data Analysis, at undergraduate and post-graduate (Master, PhD) levels, and member of the Artificial Intelligence and Decision Support Lab (LIAAD) of INESC-TEC. I hold a doctorate degree in Applied Mathematics from the University of Paris Dauphine (1991).

My current research focuses on the analysis of multidimensional complex data, known as symbolic data - data representing inherent variability, in the form of intervals or distributions - for which I develop statistical approaches and multivariate analysis methodologies.  I am generally interested in multivariate data analysis, with particular incidence in clustering methods.

Interest
Topics
Details

Details

  • Name

    Paula Brito
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2008
Publications

2018

Outlier detection in interval data

Authors
Duarte Silva, APD; Filzmoser, P; Brito, P;

Publication
Advances in Data Analysis and Classification

Abstract
A multivariate outlier detection method for interval data is proposed that makes use of a parametric approach to model the interval data. The trimmed maximum likelihood principle is adapted in order to robustly estimate the model parameters. A simulation study demonstrates the usefulness of the robust estimates for outlier detection, and new diagnostic plots allow gaining deeper insight into the structure of real world interval data. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature

2017

Off the beaten track: A new linear model for interval data

Authors
Dias, S; Brito, P;

Publication
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
We propose a new linear regression model for interval-valued variables. The model uses quantile functions to represent the intervals, thereby considering the distributions within them. In this paper we study the special case where the Uniform distribution is assumed in each observed interval, and we analyze the extension to the Symmetric Triangular distribution. The parameters of the model are obtained solving a constrained quadratic optimization problem that uses the Mallows distance between quantile functions. As in the classical case, a goodness-of-fit measure is deduced. Two applications on up-to-date fields are presented: one predicting duration of unemployment and the other allowing forecasting burned area by forest fires.

2017

Exploratory data analysis for interval compositional data

Authors
Hron, Karel; Brito, Paula; Filzmoser, Peter;

Publication
Adv. Data Analysis and Classification

Abstract
Compositional data are considered as data where relative contributions of parts on a whole, conveyed by (log-)ratios between them, are essential for the analysis. In Symbolic Data Analysis (SDA), we are in the framework of interval data when elements are characterized by variables whose values are intervals on (Formula presented.) representing inherent variability. In this paper, we address the special problem of the analysis of interval compositions, i.e., when the interval data are obtained by the aggregation of compositions. It is assumed that the interval information is represented by the respective midpoints and ranges, and both sources of information are considered as compositions. In this context, we introduce the representation of interval data as three-way data. In the framework of the log-ratio approach from compositional data analysis, it is outlined how interval compositions can be treated in an exploratory context. The goal of the analysis is to represent the compositions by coordinates which are interpretable in terms of the original compositional parts. This is achieved by summarizing all relative information (logratios) about each part into one coordinate from the coordinate system. Based on an example from the European Union Statistics on Income and Living Conditions (EU-SILC), several possibilities for an exploratory data analysis approach for interval compositions are outlined and investigated. © 2016 Springer-Verlag Berlin Heidelberg

2017

Comparing Reverse Complementary Genomic Words Based on Their Distance Distributions and Frequencies

Authors
Tavares, AH; Raymaekers, J; Rousseeuw, PJ; Silva, RM; Bastos, CAC; Pinho, A; Brito, P; Afreixo, V;

Publication
Interdisciplinary Sciences: Computational Life Sciences

Abstract

2017

Dissimilar Symmetric Word Pairs in the Human Genome

Authors
Tavares, AnaHelena; Raymaekers, Jakob; Rousseeuw, PeterJ.; Silva, RaquelM.; Bastos, CarlosA.C.; Pinho, ArmandoJ.; Brito, Paula; Afreixo, Vera;

Publication
11th International Conference on Practical Applications of Computational Biology & Bioinformatics, PACBB 2017, Porto, Portugal, 21-23 June, 2017

Abstract

Supervised
thesis

2017

Análise Classificatória de Dados Distribucionais: Abordagem Simbólica e Composicional

Author
Maria do Rosário Guimarães de Almeida Moreira

Institution
UP-FEP

2017

Gender wage discrimination across Portuguese territory and its local determinants

Author
Natália Daniela Vieira da Costa e Silva

Institution
UP-FEP

2017

Determinação do Customer Lifetime Value Aplicação ao Retalho Alimentar

Author
Patrícia Manuela Martins Castro

Institution
UP-FEP

2017

Modelos de Regressão Linear para Variáveis Intervalares Uma extensão do modelo ID

Author
Pedro Jorge Correia Malaquias

Institution
UP-FEP

2017

Meta-aprendizagem no problema de seleção de algoritmo de Análise Classificatória

Author
Vânia Patrícia Pereira Serra

Institution
UP-FEP