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Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data

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  • Alfred Ultsch
  • Jörn Lötsch

Abstract

Objective: Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets. Methods: The algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits im-plement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the “important few”, is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized. Results: As a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological inter-pretation of the results and increased the fraction of valid information that was obtained from the experimental data. Conclusions: The method is applicable to many further biomedical problems in-cluding the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data.

Suggested Citation

  • Alfred Ultsch & Jörn Lötsch, 2015. "Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.
    • Handle: RePEc:plo:pone00:0129767
    DOI: 10.1371/journal.pone.0129767
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    References listed on IDEAS

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    1. Gastwirth, Joseph L & Glauberman, Marcia, 1976. "The Interpolation of the Lorenz Curve and Gini Index from Grouped Data," Econometrica, Econometric Society, vol. 44(3), pages 479-483, May.
    2. Gastwirth, Joseph L, 1971. "A General Definition of the Lorenz Curve," Econometrica, Econometric Society, vol. 39(6), pages 1037-1039, November.
    3. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
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    2. Jan Michael Spoor, 2023. "Improving customer segmentation via classification of key accounts as outliers," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 747-760, December.

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