A two-stage principal component analysis of symbolic data using equicorrelated and jointly equicorrelated covariance structures

Anuradha Roy
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Anuradha Roy: UTSA

Working Papers from College of Business, University of Texas at San Antonio

Abstract: A new approach to derive the principal components of symbolic data is proposed in this article. This is done in two stages: first getting eigenblocks and eigenmatrices of the variance-covariance matrix, and then analyzing these eigenblocks and the corresponding principal vec- tors together in some seemly sense to get the adjusted eigenvalues and the corresponding eigenvectors of the interval data. The proposed method is very efficient in two-level and three-level symbolic data sets. Results illustrating the accuracy and appropriateness of the new method over the existing methods are presented. We have clearly shown with the help of examples that our proposed method for principal component analysis (PCA) of three-level symbolic data generalizes the commonly used PCA for multivariate data.

Keywords: Jointly equicorrelated covariance structure; symbolic data; Two-stage principal com- ponent analysis (search for similar items in EconPapers)
JEL-codes: C13 C30 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2014
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Citations: View citations in EconPapers (3)

Published in Review of Economics, March 1999, pages 1-23

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