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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 3/2019

25.07.2018 | Regular Article

Regression trees for detecting preference patterns from rank data

verfasst von: Yu-Shan Shih, Kuang-Hsun Liu

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 3/2019

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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.
Vorheriger Artikel On support vector machines under a multiple-cost scenario
Nächster Artikel Generalised linear model trees with global additive effects

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Fußnoten
1
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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Metadaten
Titel
Regression trees for detecting preference patterns from rank data
verfasst von
Yu-Shan Shih
Kuang-Hsun Liu
Publikationsdatum
25.07.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 3/2019
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-018-0332-3

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