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Deriving optimal data-analytic regimes from benchmarking studies

Author

Listed:
  • Doove, Lisa L.
  • Wilderjans, Tom F.
  • Calcagnì, Antonio
  • Van Mechelen, Iven

Abstract

In benchmarking studies with simulated data sets in which two or more statistical methods are compared, over and above the search of a universally winning method, one may investigate how the winning method may vary over patterns of characteristics of the data or the data-generating mechanism. Interestingly, this problem bears strong formal similarities to the problem of looking for optimal treatment regimes in biostatistics when two or more treatment alternatives are available for the same medical problem or disease. It is outlined how optimal data-analytic regimes, that is to say, rules for optimally calling in statistical methods, can be derived from benchmarking studies with simulated data by means of supervised classification methods (e.g., classification trees). The approach is illustrated by means of analyses of data from a benchmarking study to compare two different algorithms for the estimation of a two-mode additive clustering model.

Suggested Citation

  • Doove, Lisa L. & Wilderjans, Tom F. & Calcagnì, Antonio & Van Mechelen, Iven, 2017. "Deriving optimal data-analytic regimes from benchmarking studies," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 81-91.
    • Handle: RePEc:eee:csdana:v:107:y:2017:i:c:p:81-91
    DOI: 10.1016/j.csda.2016.10.016
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    References listed on IDEAS

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    1. Schepers, Jan & van Mechelen, Iven & Ceulemans, Eva, 2006. "Three-mode partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1623-1642, December.
    2. Anne-Laure Boulesteix & Robert Hable & Sabine Lauer & Manuel J. A. Eugster, 2015. "A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 201-212, August.
    3. Tom Wilderjans & Dirk Depril & Iven Van Mechelen, 2013. "Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 56-74, April.
    4. Juliet Shaffer, 1991. "Probability of directional errors with disordinal (qualitative) interaction," Psychometrika, Springer;The Psychometric Society, vol. 56(1), pages 29-38, March.
    5. Depril, Dirk & Van Mechelen, Iven & Mirkin, Boris, 2008. "Algorithms for additive clustering of rectangular data tables," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4923-4938, July.
    6. Wayne Desarbo, 1982. "Gennclus: New models for general nonhierarchical clustering analysis," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 449-475, December.
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