Off the beaten track: A new linear model for interval data

Sónia Dias and Paula Brito

European Journal of Operational Research, 2017, vol. 258, issue 3, 1118-1130

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.

Keywords: Interval data; Linear regression; Symbolic Data Analysis; Quantile functions (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:258:y:2017:i:3:p:1118-1130

DOI: 10.1016/j.ejor.2016.09.006

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