Linear Regression Analysis for Interval-valued Data Based on Set Arithmetic: A Review

  • Angela Blanco-Fernández
  • Ana Colubi
  • Gil González-Rodríguez
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)

Abstract

When working with real-valued data regression analysis allows to model and forecast the values of a random variable in terms of the values of either another one or several other random variables defined on the same probability space. When data are not real-valued, regression techniques should be extended and adapted to model simply relationships in an effective way. Different kinds of imprecision may appear in experimental data: uncertainty in the quantification of the data, subjective measurements, perceptions, to name but a few. Compact intervals can be effectively used to represent these imprecise data. Set- and fuzzy-valued elements are also employed for representing different kinds of imprecise data. In this paper several linear regression estimation techniques for interval-valued data are revised. Both the practical applicability and the empirical behaviour of the estimation methods is studied by comparing the performance of the techniques under different population conditions.

Keywords

Regression Problem Interval Arithmetic Random Interval Fuzzy Random Variable Simple Linear Model 
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 GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Angela Blanco-Fernández
    • 1
  • Ana Colubi
    • 1
  • Gil González-Rodríguez
    • 1
  1. 1.Department of Statistics and Operational ResearchUniversity of OviedoOviedoSpain

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