Скачать или смотреть [CSDS 2022] Short course 2 - Symbolic Data Analysis - Paula Brito and Pedro Silva

Paula Brito
University of Porto, Portugal
Bio: Paula Brito is Associate Professor at the Faculty of Economics of the University of Porto, and member of the Artificial Intelligence and Decision Support Research Group (LIAAD) of INESC TEC, Portugal. She holds a doctorate degree in Applied Mathematics from the University Paris Dauphine, and a Habilitation in Applied Mathematics from the University of Porto. Her current research focuses on the analysis of multidimensional complex data, known as symbolic data, for which she develops statistical approaches and multivariate analysis methodologies. She has been involved in two European research projects and coordinated the Portuguese participation in the H2020 FinTech project. Paula Brito was president of the International Association for Statistical Computing (IASC-ISI) in 2013-2015. She has authored a large number of papers in highly ranked journals in her field, has been invited speaker at several international conferences, is regularly member of international program committees, and has been chair of the international conferences COMPSTAT 2008 and IFCS 2022.

Pedro Duarte Silva
The Catholic Porto Business School, Portugal
Bio: Pedro Duarte Silva is an Associate Professor at the Catholic Porto Business School, and member of its research center (CEGE). He holds a doctorate degree in Business Administration from the Terry College of Business of the University of Georgia. His research focuses on the intersection between Data Analysis and Machine Learning, Multivariate Statistics and Operations Research, with a particular focus on the development of novel methodologies for the analysis of big and complex data. He is the author of numerous communications at reputed scientific conferences and his research has been published in highly ranked scientific journals such as The European Journal of Operation Research, Computational Statistic and Data Analysis, Decision Sciences, Computational Statistics, and The Journal of Multivariate Analysis.

Title: Symbolic Data Analysis: Parametric Multivariate Analysis of Interval Data

Abstract: Symbolic Data is concerned with analysing data with intrinsic variability, which is to be taken into account. In Data Mining, Multivariate Data Analysis and classical Statistics, the elements under analysis are generally individual entities for which a single value is recorded for each variable - e.g., individuals, described by age, salary, education level, etc. But when the elements of interest are classes or groups of some kind - the citizens living in given towns; car models, rather than specific vehicles - then there is variability inherent to the data. Symbolic data goes beyond the usual data representation model, considering variables whose observed values for each element are no longer necessarily single real values or categories, but may assume the form of sets, intervals, or, more generally, distributions. In this Tutorial we focus on the analysis of interval data, i.e., when the variables’ values are intervals of IR, adopting a parametric approach. The proposed modelling allows for multivariate parametric analysis; in particular M(ANOVA), discriminant analysis, model-based clustering, robust estimation and outlier detection are addressed. The referred modelling and methods are implemented in the R package MAINT.Data, available on CRAN.