In the
network technology era, the collected data are growing more and more complex,
and become larger than before. In this article, we focus on estimates of the
linear regression parameters for symbolic interval data. We propose two
approaches to estimate regression parameters for symbolic interval data under
two different data models and compare our proposed approaches with the existing
methods via simulations. Finally, we analyze two real datasets with the
proposed methods for illustrations.
References
[1]
Diday, E. (1987) Introduction à l’ Approache Symbolique en Analyse des Données. Premières Journées Symbolique-Numérique. CEREMADE, Université Paris, 21-56.
[2]
Billard, L. and Diday, E. (2000) Regression Analysis for Interval-Valued Data. In: Bock, H.-H. and Diday, E., Eds., Data Analysis, Classification and Related Methods, Springer-Verlag, Berlin, 369-374. https://doi.org/10.1007/978-3-642-59789-3_58
[3]
Carvalho, F.A.T., Neto, L. and Tenorio, C.P. (2004) A New Method to Fit a Linear Regression Model for Interval-Valued Data. Annual Conference on Artificial Intelligence: KI2004 Advances in Artical Inteligence, Ulm, 20-24 September 2004, 295-306. https://doi.org/10.1007/978-3-540-30221-6_23
[4]
Xu, W. (2010) Symbolic Data Analysis: Interval-Valued Data Regression. Ph.D. Thesis, The University of Georgia, Athens.
[5]
Billard, L. (2008) Sample Covariance Functions for Complex Quantitative Data. In: Mizuta, M. and Nakano, J., Eds., Proceedings of the International Association of Statistical Computing Conference 2008, 157-163.
[6]
Billard, L. and Diday, E. (2004) Symbolic Data Analysis: Definitions and Examples. Technical Report.
