Change Point Detection in Pairwise Comparison Data with Covariates

TL;DR

This paper proposes a new model, PS-CARE, for detecting change points in time-evolving pairwise comparison data with covariates, improving ranking accuracy and identifying shifts over time.

Contribution

The paper introduces the first method for change point detection in pairwise comparison data incorporating covariates, using MDL and PELT for model selection and optimization.

Findings

Accurately detects change points in simulated data.

Effectively captures ranking shifts in NBA dataset.

Demonstrates practical utility in real-world applications.

Abstract

This paper introduces the novel piecewise stationary covariate-assisted ranking estimation (PS-CARE) model for analyzing time-evolving pairwise comparison data, enhancing item ranking accuracy through the integration of covariate information. By partitioning the data into distinct, stationary segments, the PS-CARE model adeptly detects temporal shifts in item rankings, known as change points, whose number and positions are initially unknown. Leveraging the minimum description length (MDL) principle, this paper establishes a statistically consistent model selection criterion to estimate these unknowns. The practical optimization of this MDL criterion is done with the pruned exact linear time (PELT) algorithm. Empirical evaluations reveal the method's promising performance in accurately locating change points across various simulated scenarios. An application to an NBA dataset yielded meaningful insights that aligned with significant historical events, highlighting the method's practical utility and the MDL criterion's effectiveness in capturing temporal ranking changes. To the best of the authors' knowledge, this research pioneers change point detection in pairwise comparison data with covariate information, representing a significant leap forward in the field of dynamic ranking analysis.