Poster

Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold

Wenkai Xu · Takeru Matsuda

Virtual

Keywords: [ Active Learning ] [ Kernel Methods ] [ Algorithms ] [ Classification ]

[ Abstract ]
[ Slides
[ Paper ]
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Wed 21 Jul 9 a.m. PDT — 11 a.m. PDT
 
Spotlight presentation: Kernel Methods
Wed 21 Jul 7 a.m. PDT — 8 a.m. PDT

Abstract:

In many applications, we encounter data on Riemannian manifolds such as torus and rotation groups. Standard statistical procedures for multivariate data are not applicable to such data. In this study, we develop goodness-of-fit testing and interpretable model criticism methods for general distributions on Riemannian manifolds, including those with an intractable normalization constant. The proposed methods are based on extensions of kernel Stein discrepancy, which are derived from Stein operators on Riemannian manifolds. We discuss the connections between the proposed tests with existing ones and provide a theoretical analysis of their asymptotic Bahadur efficiency. Simulation results and real data applications show the validity and usefulness of the proposed methods.

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