Mallows Ranking Models: Maximum Likelihood Estimate and Regeneration
Abstract
This paper is concerned with various Mallows ranking models. We study the statistical properties of the MLE of Mallows’ $\phi$ model. We also make connections of various Mallows ranking models, encompassing recent progress in mathematics. Motivated by the infinite top-$t$ ranking model, we propose an algorithm to select the model size $t$ automatically. The key idea relies on the renewal property of such an infinite random permutation. Our algorithm shows good performance on several data sets.
Cite
Text
Tang. "Mallows Ranking Models: Maximum Likelihood Estimate and Regeneration." International Conference on Machine Learning, 2019.Markdown
[Tang. "Mallows Ranking Models: Maximum Likelihood Estimate and Regeneration." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/tang2019icml-mallows/)BibTeX
@inproceedings{tang2019icml-mallows,
title = {{Mallows Ranking Models: Maximum Likelihood Estimate and Regeneration}},
author = {Tang, Wenpin},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {6125-6134},
volume = {97},
url = {https://mlanthology.org/icml/2019/tang2019icml-mallows/}
}