Regression trees for detecting preference patterns from rank data

Yu-Shan Shih () and Kuang-Hsun Liu ()
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Yu-Shan Shih: National Chung Cheng University
Kuang-Hsun Liu: XDM Technology

Advances in Data Analysis and Classification, 2019, vol. 13, issue 3, No 6, 683-702

Abstract: 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.

Keywords: GUIDE regression tree; Machine learning; Missing values; Paired comparison; Scoring system; Selection bias; 62G08 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11634-018-0332-3

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