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About

About

I am a professor at the Scientific Area of Mathematics in the School of Technology and Management of the Polytechnic Institute of Viana do Castelo (ESTG-IPVC) and member of the Laboratory in Artificial Intelligence and Decision Support (LIAAD – INESC TEC) of the University of Porto. I have a Msc in Mathematics by the University of Minho and a PhD in Applied Mathematics by the University of Porto in 2014.

My main research lines are Data Analysis; Symbolic Data Analysis (Analysis of multidimensional complex data) and Linear regression models. I work in the development and application of methods adapted to data carrying a lot of information. This research is included in the framework of Symbolic Data Analysis.

Moreover, I collaborate with bio-informaticians and chemistry researchers for the development of mathematical models applied to agent-based modelling. 

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Publications

2022

Analysis of Distributional Data

Authors
Brito, P; Dias, S;

Publication

Abstract

2022

Regression Analysis with the Distribution and Symmetric Distribution Model

Authors
Dias, S; Brito, P;

Publication
Analysis of Distributional Data

Abstract

2022

Descriptive Statistics based on Frequency Distribution

Authors
Dias, S; Brito, P;

Publication
Analysis of Distributional Data

Abstract

2022

Fundamental Concepts about Distributional Data

Authors
Dias, S; Brito, P;

Publication
Analysis of Distributional Data

Abstract

2021

Discriminant analysis of distributional data via fractional programming

Authors
Dias, S; Brito, P; Amaral, P;

Publication
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
We address classification of distributional data, where units are described by histogram or interval-valued variables. The proposed approach uses a linear discriminant function where distributions or intervals are represented by quantile functions, under specific assumptions. This discriminant function allows defining a score for each unit, in the form of a quantile function, which is used to classify the units in two a priori groups, using the Mallows distance. There is a diversity of application areas for the proposed linear discriminant method. In this work we classify the airline companies operating in NY airports based on air time and arrival/departure delays, using a full year flights. © 2021 Elsevier B.V.