Distance-based tree models for ranking data
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.
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- Kenneth Train, 2003.
"Discrete Choice Methods with Simulation,"
Online economics textbooks,
SUNY-Oswego, Department of Economics, number emetr2.
- 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.
- Philip Yu, 2000. "Bayesian analysis of order-statistics models for ranking data," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 281-299, September.
- 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.
- 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.
- Ahn, Hongshik, 1996. "Log-normal regression modeling through recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 21(4), pages 381-398, April.
- Agostino Tarsitano, 2009. "Comparing The Effectiveness Of Rank Correlation Statistics," Working Papers 200906, Università della Calabria, Dipartimento di Economia, Statistica e Finanza (Ex Dipartimento di Economia e Statistica).
- 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.
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