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Distance-based tree models for ranking data


  • Lee, Paul H.
  • Yu, Philip L.H.


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

    1. Ahn, Hongshik, 1996. "Log-normal regression modeling through recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 21(4), pages 381-398, April.
    2. Agostino Tarsitano, 2009. "Comparing The Effectiveness Of Rank Correlation Statistics," Working Papers 200906, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, March.
    4. Hausman, Jerry A. & Ruud, Paul A., 1987. "Specifying and testing econometric models for rank-ordered data," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 83-104.
    5. Francesco Audrino & Peter Bühlmann, 2001. "Tree-structured generalized autoregressive conditional heteroscedastic models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 727-744.
    6. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
    7. Murphy, Thomas Brendan & Martin, Donal, 2003. "Mixtures of distance-based models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 645-655, January.
    8. Philip Yu, 2000. "Bayesian analysis of order-statistics models for ranking data," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 281-299, September.
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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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