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When observations are truncated, we are limited to an incomplete picture ofour dataset. Recent methods deal with truncated density estimation problems byturning to score matching, where the access to the intractable normalisingconstant is not required. We present a novel extension to truncated scorematching for a Riemannian manifold. Applications are presented for the vonMises-Fisher and Kent distributions on a two dimensional sphere in $\R^3$, aswell as a real-world application of extreme storm observations in the USA. Insimulated data experiments, our score matching estimator is able to approximatethe true parameter values with a low estimation error and shows improvementsover a maximum likelihood estimator.

Score Matching for Truncated Density Estimation on a Manifold