Abstract
Several linear regression models involving interval-valued variables have been formalized based on the interval arithmetic. In this work, a new linear regression model with interval-valued response and real predictor based on the interval arithmetic is formally described. The least-squares estimation of the model is solved by means of a constrained minimization problem which guarantees the coherency of the estimators with the regression parameters. The practical applicability of the estimation method is checked on a real-life example, and the empirical behaviour of the procedure is shown by means of some simulation studies.
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Blanco-Fernández, A., Colubi, A., García-Bárzana, M., Montenegro, M. (2013). A Linear Regression Model for Interval-Valued Response Based on Set Arithmetic. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_12
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DOI: https://doi.org/10.1007/978-3-642-33042-1_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33041-4
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