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Robust principal component analysis in Stata

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  • Vincenzo Verardi

    (University of Brussels and University of Namur)

Abstract

When some observations are outlying (in one or several dimensions) PCA is distorted an may lead to incorrect results. We therefore propose a simple solution to deal with this problem by providing a short ado file. To illustrate the importance of outliers in PCA I would like to present a simple analysis identifying the underlying factors of academic excellence calling both the classical PCA and the robust PCA and relying on the rankings of Universities.

Suggested Citation

  • Vincenzo Verardi, 2009. "Robust principal component analysis in Stata," United Kingdom Stata Users' Group Meetings 2009 02, Stata Users Group.
    • Handle: RePEc:boc:usug09:02
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    File URL: http://repec.org/usug2009/Verardi.ppt
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    Cited by:

    1. Ben Slimane, Mehdi & Huchet-Bourdon, Marilyne & Zitouna, Habib, 2016. "The role of sectoral FDI in promoting agricultural production and improving food security," International Economics, Elsevier, vol. 145(C), pages 50-65.

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