Robust regression with application to symbolic interval data

Authors:
Roberta A. A. Fagundes;Renata M. C. R. De Souza;Francisco José A. Cysneiros
Affiliations:
Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, CEP 50740-560 Recife (PE), Brazil;Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, CEP 50740-560 Recife (PE), Brazil;Departamento de Estatística, Centro de Ciências Exatas, Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, s/n, Cidade Universitária, CEP 50740-540 Recife (PE), Brazil
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

This paper presents a robust regression model that deals with cases that have interval-valued outliers in the input data set. Each interval of the input data is represented by its range and midpoint and the fitting to interval-valued data is not sensible in the presence of midpoint and/or range outliers on the interval response. The predictions of the lower and upper bounds of new intervals are performed and simulation studies are carried out to validate these predictions. Two applications with real-life interval data sets are considered. The prediction quality is assessed by a mean magnitude of relative error calculated from a test data set.