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• W3134040191 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 of the proposed methods." @default.
• W3134040191 created "2021-03-15" @default.
• W3134040191 creator A5045812199 @default.
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• W3134040191 date "2021-03-01" @default.
• W3134040191 modified "2023-09-27" @default.
• W3134040191 title "Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds." @default.
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• W3134040191 hasPublicationYear "2021" @default.
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