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On the use of archetypes as benchmarks

Author

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  • Giovanni C. Porzio
  • Giancarlo Ragozini
  • Domenico Vistocco

Abstract

Benchmarking plays a relevant role in performance analysis, and statistical methods can be fruitfully exploited for its aims. While clustering, regression, and frontier analysis may serve some benchmarking purposes, we propose to consider archetypal analysis as a suitable technique. Archetypes are extreme points that synthesize data and that, in our opinion, can be profitably used as benchmarks. That is, they may be viewed as key reference performers in the comparison process. We suggest a three‐step data driven benchmarking procedure, which enables users: (i) to identify some reference performers, (ii) to analyze their features, (iii) to compare observed performers with them. An exploratory point of view is preferred, and graphical devices are adopted throughout the procedure. Finally, our approach is presented by means of an illustrative example based on The Times league table of the world top 200 universities. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Giovanni C. Porzio & Giancarlo Ragozini & Domenico Vistocco, 2008. "On the use of archetypes as benchmarks," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 419-437, September.
    • Handle: RePEc:wly:apsmbi:v:24:y:2008:i:5:p:419-437
    DOI: 10.1002/asmb.727
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    References listed on IDEAS

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    4. Peter Smith, 1990. "The Use of Performance Indicators in the Public Sector," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(1), pages 53-72, January.
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    1. Moliner, Jesús & Epifanio, Irene, 2019. "Robust multivariate and functional archetypal analysis with application to financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 195-208.
    2. Irene Epifanio & María Victoria Ibáñez & Amelia Simó, 2018. "Archetypal shapes based on landmarks and extension to handle missing data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 705-735, September.
    3. Eugster, Manuel J.A. & Leisch, Friedrich, 2011. "Weighted and robust archetypal analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1215-1225, March.
    4. Seiler, Christian & Wohlrabe, Klaus, 2013. "Archetypal scientists," Journal of Informetrics, Elsevier, vol. 7(2), pages 345-356.
    5. Paola Costantini & Marielle Linting & Giovanni C. Porzio, 2010. "Mining performance data through nonlinear PCA with optimal scaling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(1), pages 85-101, January.
    6. Eugster, Manuel J. A. & Leisch, Friedrich, 2009. "From Spider-Man to Hero — Archetypal Analysis in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i08).

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