Detecting Abrupt Changes in Sequential Pairwise Comparison Data

Wanshan Li · Alessandro Rinaldo · Daren Wang

Hall J #432

Keywords: [ high-dimensional statistics ] [ ranking ] [ change point detection ] [ Bradley-Terry model ]

[ Abstract ]
[ Poster [ OpenReview
Thu 1 Dec 9 a.m. PST — 11 a.m. PST
Spotlight presentation: Lightning Talks 1B-4
Tue 6 Dec 10:30 a.m. PST — 10:45 a.m. PST


The Bradley-Terry-Luce (BTL) model is a classic and very popular statistical approach for eliciting a global ranking among a collection of items using pairwise comparison data. In applications in which the comparison outcomes are observed as a time series, it is often the case that data are non-stationary, in the sense that the true underlying ranking changes over time. In this paper we are concerned with localizing the change points in a high-dimensional BTL model with piece-wise constant parameters. We propose novel and practicable algorithms based on dynamic programming that can consistently estimate the unknown locations of the change points. We provide consistency rates for our methodology that depend explicitly on the model parameters, the temporal spacing between two consecutive change points and the magnitude of the change. We corroborate our findings with extensive numerical experiments and a real-life example.

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