Distance-based tree models for ranking data

Paul H. LEE, Leung Ho Philip YU

Research output: Contribution to journalArticles

29 Citations (Scopus)

Abstract

Ranking data has applications in different fields of studies, like marketing, psychology and politics. Over the years, many models for ranking data have been developed. Among them, distance-based ranking models, which originate from the classical rank correlations, postulate that the probability of observing a ranking of items depends on the distance between the observed ranking and a modal ranking. The closer to the modal ranking, the higher the ranking probability is. However, such a model basically assumes a homogeneous population and does not incorporate the presence of covariates.

To overcome these limitations, we combine the strength of a tree model and the existing distance-based models to build a model that can handle more complexity and improve prediction accuracy. We will introduce a recursive partitioning algorithm for building a tree model with a distance-based ranking model fitted at each leaf. We will also consider new weighted distance measures which allow different weights for different ranks in formulating more flexible distance-based tree models. Finally, we will apply the proposed methodology to analyze a ranking dataset of Inglehart's items collected in the 1999 European Values Studies. Copyright © 2010 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)1672-1682
JournalComputational Statistics and Data Analysis
Volume54
Issue number6
Early online dateFeb 2010
DOIs
Publication statusPublished - Jun 2010

Citation

Lee, P. H., & Yu, P. L. H. (2010). Distance-based tree models for ranking data. Computational Statistics and Data Analysis, 54(6), 1672-1682. doi: 10.1016/j.csda.2010.01.027

Keywords

  • Decision tree
  • Ranking data
  • Distance-based model

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