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Found 4 papers, 2 papers with code
On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection
This paper considers the use of recently proposed optimal transport-based multivariate test statistics, namely rank energy and its variant the soft rank energy derived from entropically regularized optimal transport, for the unsupervised nonparametric change point detection (CPD) problem.
Robust and efficient change point detection using novel multivariate rank-energy GoF test
In this paper, we use and further develop upon a recently proposed multivariate, distribution-free Goodness-of-Fit (GoF) test based on the theory of Optimal Transport (OT) called the Rank Energy (RE) [1], for non-parametric and unsupervised Change Point Detection (CPD) in multivariate time series data.
Multivariate rank via entropic optimal transport: sample efficiency and generative modeling
We leverage this result to demonstrate fast convergence of sample sRE and sRMMD to their population version making them useful for high-dimensional GoF testing.
Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport
In this paper, we extend the recently proposed multivariate rank energy distance, based on the theory of optimal transport, for statistical testing of distributional similarity, to soft rank energy distance.
