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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
001
Publications

2019

Clustering genomic words in human DNA using peaks and trends of distributions

Authors
Tavares, AH; Raymaekers, J; Rousseeuw, PJ; Brito, P; Afreixo, V;

Publication
Advances in Data Analysis and Classification

Abstract
In this work we seek clusters of genomic words in human DNA by studying their inter-word lag distributions. Due to the particularly spiked nature of these histograms, a clustering procedure is proposed that first decomposes each distribution into a baseline and a peak distribution. An outlier-robust fitting method is used to estimate the baseline distribution (the ‘trend’), and a sparse vector of detrended data captures the peak structure. A simulation study demonstrates the effectiveness of the clustering procedure in grouping distributions with similar peak behavior and/or baseline features. The procedure is applied to investigate similarities between the distribution patterns of genomic words of lengths 3 and 5 in the human genome. These experiments demonstrate the potential of the new method for identifying words with similar distance patterns. © 2019, The Author(s).

2019

Clustering of interval time series

Authors
Maharaj, EA; Teles, P; Brito, P;

Publication
Statistics and Computing

Abstract
Interval time series occur when real intervals of some variable of interest are registered as an ordered sequence along time. We address the problem of clustering interval time series (ITS), for which different approaches are proposed. First, clustering is performed based on point-to-point comparisons. Time-domain and wavelet features also serve as clustering variables in alternative approaches. Furthermore, autocorrelation matrix functions, gathering the autocorrelation and cross-correlation functions of the ITS upper and lower bounds, may be compared using adequate distances (e.g. the Frobenius distance) and used for clustering ITS. An improved procedure to determine the autocorrelation function of ITS is proposed, which also serves as a basis for clustering. The different alternative approaches are explored and their performances compared for ITS simulated under different setups. An application to sea level daily ranges, observed at different locations in Australia, illustrates the proposed methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

2018

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

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.

Supervised
thesis

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

2017

Analysis of inter genomic word distance distributions

Author
Ana Helena Marques de Pinho Tavares

Institution
UA