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Regression trees for detecting preference patterns from rank data

Author

Listed:
  • Yu-Shan Shih

    (National Chung Cheng University)

  • Kuang-Hsun Liu

    (XDM Technology)

Abstract

A regression tree method for analyzing rank data is proposed. A key ingredient of the methodology is to convert ranks into scores by paired comparison. We then utilize the GUIDE tree method on the score vectors to identify the preference patterns in the data. This method is exempt from selection bias and the simulation results show that it is good with respect to the selection of split variables and has a better prediction accuracy than the two other investigated methods in some cases. Furthermore, it is applicable to complex data which may contain incomplete ranks and missing covariate values. We demonstrate its usefulness in two real data studies.

Suggested Citation

  • Yu-Shan Shih & Kuang-Hsun Liu, 2019. "Regression trees for detecting preference patterns from rank 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. 13(3), pages 683-702, September.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:3:d:10.1007_s11634-018-0332-3
    DOI: 10.1007/s11634-018-0332-3
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    References listed on IDEAS

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    6. Hatzinger, Reinhold & Dittrich, Regina, 2012. "prefmod: An R Package for Modeling Preferences Based on Paired Comparisons, Rankings, or Ratings," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i10).
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