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Consistency and asymptotic normality of M-estimates of scatter on Grassmann manifolds

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This work proposes a study of M-estimates of scatter for matrix angular central Gaussian distributions on Grassmann manifold G(m,r) of all vector subspaces of dimension r of Rm. Such distributions are associated to random subspaces generated by r i.i.d. multivariate centred normal random vectors, and are of interest in Bayesian model selection for cointegration. We provide a careful study of the existence and the unicity of such M-estimators using geometrical arguments, and then study their consistency and asymptotic normality.

Original languageEnglish
Article number104998
JournalJournal of Multivariate Analysis
Publication statusPublished - Jul 2022

    Research areas

  • Covariance matrix, Grassmann manifold, Matrix central angular distribution

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