A robust method for linear regression of symbolic interval data

Authors:
Marco A. O. Domingues;Renata M. C. R. de Souza;Francisco José A. Cysneiros
Affiliations:
Centro de Informática, Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, s/n, Cidade Universitária, CEP 50740-540, Recife (PE), Brazil;Centro de Informática, Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, s/n, Cidade Universitária, CEP 50740-540, Recife (PE), Brazil;Departamento de Estatstica, CCEN, Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, s/n, Cidade Universitária, CEP 50740-540, Recife (PE), Brazil
Venue:
Pattern Recognition Letters
Year:
2010

Citing 3
Cited 3

Centre and Range method for fitting a linear regression model to symbolic interval data

Computational Statistics & Data Analysis
Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software
Restricted methods in symmetrical linear regression models

Computational Statistics & Data Analysis

Likelihood-based Imprecise Regression

International Journal of Approximate Reasoning
Robust regression with application to symbolic interval data

Engineering Applications of Artificial Intelligence
Interval kernel regression

Neurocomputing

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Abstract

This paper introduces a new linear regression method for interval valued-data. The method is based on the symmetrical linear regression methodology such that the prediction of the lower and upper bounds of the interval value of the dependent variable is not damaged by the presence of interval-valued data outliers. The method considers mid-points and ranges of the interval values assumed by the variables in the learning set. The prediction of the boundaries of an interval is accomplished through a combination of predictions from mid-point and range of the interval values. The evaluation of the method is based on the average behavior of a pooled root mean-square error. Experiments with real and simulated symbolic interval data sets demonstrate the usefulness of this symbolic symmetrical linear regression method.