A least squares approach to Principal Component Analysis for interval valued data
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References listed on IDEAS
- Timmerman, Marieke E. & Kiers, Henk A. L., 2002. "Three-way component analysis with smoothness constraints," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 447-470, September.
- Giordani, Paolo & Kiers, Henk A. L., 2004. "Principal Component Analysis of symmetric fuzzy data," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 519-548, April.
- A. Meltzer & Peter Ordeshook & Thomas Romer, 1983. "Introduction," Public Choice, Springer, vol. 41(1), pages 1-5, January.
- Roger Millsap & William Meredith, 1988. "Component analysis in cross-sectional and longitudinal data," Psychometrika, Springer;The Psychometric Society, vol. 53(1), pages 123-134, March.
- D'Urso, Pierpaolo & Gastaldi, Tommaso, 2000. "A least-squares approach to fuzzy linear regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 427-440, October.
- Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
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CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Pierpaolo D'Urso & Paolo Giordani, 2006. "A robust fuzzy k-means clustering model for interval valued data," Computational Statistics, Springer, vol. 21(2), pages 251-269, June.
- Antonio Irpino & Valentino Tontodonato, 2006. "Clustering reduced interval data using Hausdorff distance," Computational Statistics, Springer, vol. 21(2), pages 271-288, June.
More about this item
KeywordsPrincipal Component Analysis; Least squares approach; Interval valued data; Chemical data;
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ALL-2003-11-09 (All new papers)
- NEP-ECM-2003-11-09 (Econometrics)
- NEP-RMG-2003-11-09 (Risk Management)
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