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Standardization and Transformation in Principal Component Analysis, with Applications to Archaeometry

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  • M. J. Baxter

Abstract

Principal component analysis is commonly used in archaeometric applications to identify or display structure in the chemical composition of archaeological artefacts. A recurring topic of debate is whether, and how, data should be transformed and whether, after transformation, standardization should be used. Most discussion has focused on the use of logarithmic transformations. The merits of different approaches are investigated empirically in the paper, using 20 published data sets showing different degrees of structure. The opportunity is taken to examine the merits of the rarely used rank transformation, which has potential attractions when outliers occur or the variables are unusually distributed.

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  • M. J. Baxter, 1995. "Standardization and Transformation in Principal Component Analysis, with Applications to Archaeometry," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 513-527, December.
    • Handle: RePEc:bla:jorssc:v:44:y:1995:i:4:p:513-527
    DOI: 10.2307/2986142
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    Cited by:

    1. T. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2011. "A data-based power transformation for compositional data," MPRA Paper 53068, University Library of Munich, Germany.
    2. Ceyhun Elgin & Gökçer Özgür & Kerem Cantekin, 2023. "Measuring green technology adoption across countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(1), pages 1-11, February.
    3. Yannis Pantazis & Michail Tsagris & Andrew T. A. Wood, 2019. "Gaussian Asymptotic Limits for the α-transformation in the Analysis of Compositional Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 63-82, February.
    4. Chong, Alain Yee-Loong & Ooi, Keng-Boon & Sohal, Amrik, 2009. "The relationship between supply chain factors and adoption of e-Collaboration tools: An empirical examination," International Journal of Production Economics, Elsevier, vol. 122(1), pages 150-160, November.
    5. Chrys Caroni & Nedret Billor, 2007. "Robust Detection of Multiple Outliers in Grouped Multivariate Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(10), pages 1241-1250.
    6. Chae, Seong S. & Warde, William D., 2006. "Effect of using principal coordinates and principal components on retrieval of clusters," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1407-1417, March.
    7. Rajesh Barik & Sanjaya Kumar Lenka, 2023. "Does financial inclusion control corruption in upper-middle and lower-middle income countries?," Asia-Pacific Journal of Regional Science, Springer, vol. 7(1), pages 69-92, March.

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