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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bertoluzza, C., Corral, N., Salas, A.: On a new class of distances between fuzzy numbers. Mathware Soft. Comp. 2, 71–84 (1995)
Billard, L., Diday, E.: From the Statistics of data to the Statistics of knowledge: Symbolic Data Analysis. J. Amer. Stat. Assoc. 98, 470–487 (2003)
Blanco-Fernández, A., Corral, N., González-Rodríguez, G.: Estimation of a flexible simple linear model for interval data based on set arithmetic. Comp. Stat. Data Anal. 55(9), 2568–2578 (2011)
Diamond, P.: Least squares fitting of compact set-valued data. J. Math. Anal. Appl. 147, 531–544 (1990)
Gil, M.A., Lubiano, A., Montenegro, M., López-García, M.T.: Least squares fitting of an affine function and strength of association for interval-valued data. Metrika 56, 97–111 (2002)
González-Rodríguez, G., Colubi, A., Coppi, R., Giordani, P.: On the estimation of linear models with interval-valued data. In: Proc. 17th IASC. Physica-Verlag, Heidelberg (2006)
González-Rodríguez, G., Blanco, A., Corral, N., Colubi, A.: Least squares estimation of linear regression models for convex compact random sets. Adv. D. Anal. Class. 1, 67–81 (2007)
González-Rodríguez, G., Blanco, A., Colubi, A., Lubiano, M.A.: Estimation of a simple linear regression model for fuzzy random variables. Fuzzy Sets Syst. 160(3), 357–370 (2009)
Körner, R., Näther, W.: On the variance of random fuzzy variables. In: Stat. Mod. Anal. Manag. Fuzzy D., pp. 22–39. Physica-Verlag, Heidelberg (2002)
Huber, C., Solev, V., Vonta, F.: Interval censored and truncated data: Rate of convergence of NPMLE of the density. J. Stat. Plann. Infer. 139, 1734–1749 (2009)
Jahanshahloo, G.R., Hosseinzadeh, L.F., Rostamy, M.M.: A generalized model for data envelopment analysis with interval data. Appl. Math. Model 33, 3237–3244 (2008)
Lima Neto, E.A., de Carvalho, F.d.A.T.: Constrained linear regression models for symbolic interval-valued variables. Comp. Stat. Data Anal. 54, 333–347 (2010)
Trutschnig, W., González-Rodríguez, G., Colubi, A., Gil, M.A.: A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spread. Inform. Sci. 179(23), 3964–3972 (2009)
Wang, G., Zhang, Y.: The theory of fuzzy stochastic processes. Fuzzy Sets Syst. 51, 161–178 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Blanco-Fernández, A., Colubi, A., González-Rodríguez, G. (2013). Linear Regression Analysis for Interval-valued Data Based on Set Arithmetic: A Review. In: Borgelt, C., Gil, M., Sousa, J., Verleysen, M. (eds) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30278-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-30278-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30277-0
Online ISBN: 978-3-642-30278-7
eBook Packages: EngineeringEngineering (R0)