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Nonparametric Estimation in the Dynamic Bradley-Terry Model

Heejong Bong,Wanshan Li,Shamindra Shrotriya,A. Rinaldo

Published 2020 in International Conference on Artificial Intelligence and Statistics

ABSTRACT

We propose a time-varying generalization of the Bradley-Terry model that allows for nonparametric modeling of dynamic global rankings of distinct teams. We develop a novel estimator that relies on kernel smoothing to pre-process the pairwise comparisons over time and is applicable in sparse settings where the Bradley-Terry may not be fit. We obtain necessary and sufficient conditions for the existence and uniqueness of our estimator. We also derive time-varying oracle bounds for both the estimation error and the excess risk in the model-agnostic setting where the Bradley-Terry model is not necessarily the true data generating process. We thoroughly test the practical effectiveness of our model using both simulated and real world data and suggest an efficient data-driven approach for bandwidth tuning.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    International Conference on Artificial Intelligence and Statistics

  • Publication date

    2020-02-28

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 2003.00083
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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