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Distance-based tree models for ranking data

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  • Lee, Paul H.
  • Yu, Philip L.H.

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.

Suggested Citation

  • Lee, Paul H. & Yu, Philip L.H., 2010. "Distance-based tree models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1672-1682, June.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1672-1682
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Antonio D’Ambrosio & Willem J. Heiser, 2016. "A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 774-794, September.
    2. Lee, Paul H. & Yu, Philip L.H., 2012. "Mixtures of weighted distance-based models for ranking data with applications in political studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2486-2500.
    3. Kung, Yi-Hung & Lin, Chang-Ting & Shih, Yu-Shan, 2012. "Split variable selection for tree modeling on rank data," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2830-2836.
    4. Krishna Paudel & Mahesh Pandit & Michael Dunn, 2013. "Using spectral analysis and multinomial logit regression to explain households’ choice patterns," Empirical Economics, Springer, vol. 44(2), pages 739-760, April.
    5. Philip L. H. Yu & Paul H. Lee & S. F. Cheung & Esther Y. Y. Lau & Doris S. Y. Mok & Harry C. Hui, 2016. "Logit tree models for discrete choice data with application to advice-seeking preferences among Chinese Christians," Computational Statistics, Springer, vol. 31(2), pages 799-827, June.
    6. Ghimire, Ramesh & Green, Gary T. & Paudel, Krishna P. & Poudyal, Neelam C. & Cordell, H. Ken, 2017. "Visitors' Preferences for Freshwater Amenity Characteristics: Implications from the U.S. Household Survey," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 42(1), January.
    7. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.

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