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.
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Notes
Pearson’s Chi-Square test of independence and its Bonferroni-adjusted p value were used in CHAID, a classical tree method (Kass 1980).
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The authors are very grateful to the two reviewers and the editors for many helpful comments and suggestions.
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This research is supported in part by Taiwan MOST Grant 106-2118-M-194-002.
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Shih, YS., Liu, KH. Regression trees for detecting preference patterns from rank data. Adv Data Anal Classif 13, 683–702 (2019). https://doi.org/10.1007/s11634-018-0332-3
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DOI: https://doi.org/10.1007/s11634-018-0332-3