Log-normal regression modeling through recursive partitioning
Computational Statistics & Data Analysis
Mixtures of distance-based models for ranking data
Computational Statistics & Data Analysis
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Adaptive mixtures of local experts
Neural Computation
Mixtures of weighted distance-based models for ranking data with applications in political studies
Computational Statistics & Data Analysis
Split variable selection for tree modeling on rank data
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
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.