Robust clusterwise linear regression through trimming

L.A. García-Escudero, A. Gordaliza, A. Mayo-Iscar and R. San Martín

Computational Statistics & Data Analysis, 2010, vol. 54, issue 12, 3057-3069

Abstract: The presence of clusters in a data set is sometimes due to the existence of certain relations among the measured variables which vary depending on some hidden factors. In these cases, observations could be grouped in a natural way around linear and nonlinear structures and, thus, the problem of doing robust clustering around linear affine subspaces has recently been tackled through the minimization of a trimmed sum of orthogonal residuals. This "orthogonal approach" implies that there is no privileged variable playing the role of response variable or output. However, there are problems where clearly one variable is wanted to be explained in terms of the other ones and the use of vertical residuals from classical linear regression seems to be more advisable. The so-called TCLUST methodology is extended to perform robust clusterwise linear regression and a feasible algorithm for the practical implementation is proposed. The algorithm includes a "second trimming" step aimed to diminishing the effect of leverage points.

Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00249-7
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:54:y:2010:i:12:p:3057-3069

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().