Weighting Distance Matrices Using Rank Correlations
In a number of applications of multivariate analysis, the data matrix is not fully observed. Instead a set of distance matrices on the same entities is available. A reasonable strategy to construct a global distance matrix is to compute a weighted average of the partial distance matrices, provided that an appropriate system of weights can be defined. The Distatis method developed by Abdi et al. (2005) is a three-step procedure for computing the global distance matrix. An important aspect of that procedure is the computation of the vector correlation coefficient (RV) to measure the similarity between partial distance matrices. The RV coefficient is based on the Pearson product moment correlation coeffcient, which is highly prone to the effects of outliers. We are convinced that, in many measurable phenomena, the relationships between distances are far more likely to be ordinal than interval in nature, and it is therefore preferable to adopt an approach appropriate to ordinal data. The goal of our paper is to revise the system of weights of the Distatis procedure substituting the conventional Pearson coefficient with rank correlations that are less affected by errors of measurement, perturbation or presence of outliers in the data. In the light of our findings on real and simulated data sets, we recommend the use of a speci c coefficient of rank correlation to replace, where necessary, the conventional vector correlation.
|Date of creation:||Dec 2012|
|Contact details of provider:|| Postal: Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania", Ponte Pietro Bucci, Cubo 0/C, I-87036 Arcavacata di Rende, CS, Italy|
Phone: +39 0984 492413
Fax: +39 0984 492421
Web page: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Véronique Campbell & Pierre Legendre & François-Joseph Lapointe, 2009. "Assessing Congruence Among Ultrametric Distance Matrices," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 103-117, April.
- Vladimir Batagelj & Matevz Bren, 1995. "Comparing resemblance measures," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 73-90, March.
- Francis Cailliez, 1983. "The analytical solution of the additive constant problem," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 305-308, June.
When requesting a correction, please mention this item's handle: RePEc:clb:wpaper:201209. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Giovanni Dodero)
If references are entirely missing, you can add them using this form.