Off the beaten track: A new linear model for interval data Sónia Dias en Paula Brito en 2017-12-21T18:23:56Z 2017-12-21T18:23:56Z 2017 en
dc.description.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. en
dc.identifier.uri en
dc.language eng en
dc.relation 5739 en
dc.relation 4984 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Off the beaten track: A new linear model for interval data en
dc.type article en
dc.type Publication en
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