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Applying Constrained Linear Regression Models to Predict Interval-Valued Data

  • Eufrasio de A. Lima Neto
  • Francisco de A.T. de Carvalho
  • Eduarda S. Freire
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3698)

Abstract

Billard and Diday [2] were the first to present a regression method for interval-value data. De Carvalho et al [5] presented a new approach that incorporated the information contained in the ranges of the intervals and that presented a better performance when compared with the Billard and Diday method. However, both methods do not guarantee that the predicted values of the lower bounds (ŷ Li )

will be lower than the predicted values of the upper bounds (ŷ Ui ). This paper presents two approaches based on regression models with inequality constraints that guarantee the mathematical coherence between the predicted values ŷ Li and ŷ Ui . The performance of these approaches, in relation with the methods proposed by Billard and Diday [2] and De Carvalho et al [2], will be evaluated in framework of Monte Carlo experiments.

Keywords

Monte Carlo Simulation Regression Linear Model Prediction Performance Linear Regression Model Inequality Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Eufrasio de A. Lima Neto
    • 1
  • Francisco de A.T. de Carvalho
    • 1
  • Eduarda S. Freire
    • 1
  1. 1.Centro de Informatica – CIn / UFPERecifeBrasil

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