Mixtures of distance-based models for ranking data
Computational Statistics & Data Analysis
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Unsupervised rank aggregation with distance-based models
Proceedings of the 25th international conference on Machine learning
Decision tree and instance-based learning for label ranking
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Model-based cluster and discriminant analysis with the MIXMOD software
Computational Statistics & Data Analysis
Distance-based tree models for ranking data
Computational Statistics & Data Analysis
An Exponential Model for Infinite Rankings
The Journal of Machine Learning Research
A generative model for rank data based on insertion sort algorithm
Computational Statistics & Data Analysis
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Analysis of ranking data is often required in various fields of study, for example politics, market research and psychology. Over the years, many statistical models for ranking data have been developed. Among them, distance-based ranking models 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 assumes a homogeneous population, and the single dispersion parameter in the model may not be able to describe the data well. To overcome these limitations, we formulate more flexible models by considering the recently developed weighted distance-based models which can allow different weights for different ranks. The assumption of a homogeneous population can be relaxed by an extension to mixtures of weighted distance-based models. The properties of weighted distance-based models are also discussed. We carry out simulations to test the performance of our parameter estimation and model selection procedures. Finally, we apply the proposed methodology to analyze synthetic ranking datasets and a real world ranking dataset about political goals priority.