@article{Oliveira_Vilela_Pacheco_Valadas_Salvador_2017, title={Extracting Information from Interval Data Using Symbolic Principal Component Analysis}, volume={46}, url={https://www.ajs.or.at/index.php/ajs/article/view/vol46-3-4-8}, DOI={10.17713/ajs.v46i3-4.673}, abstractNote={We introduce generic definitions of symbolic variance and covariance for random interval-valued variables, that lead to a unified and insightful interpretation of four known symbolic principal component estimation methods: CPCA, VPCA, CIPCA, and SymCovPCA. Moreover, we propose the use of truncated versions of symbolic principal components, that use a strict subset of the original symbolic variables, as a way to improve the interpretation of symbolic principal components. Furthermore, the analysis of a real dataset leads to a meaningful characterization of Internet traffic applications, while highligting similarities between the symbolic principal component estimation methods considered in the paper.}, number={3-4}, journal={Austrian Journal of Statistics}, author={Oliveira, M. R. and Vilela, M. and Pacheco, A. and Valadas, Rui and Salvador, Paulo}, year={2017}, month={Apr.}, pages={79–87} }