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Diwei Zhou

  1. Non-Euclidean statistical analysis of covariance matrices and diffusion tensors.

    Ср, 10/20/2010 - 15:01 — SomNambul
    Authors: Ian L. Dryden, Diwei Zhou, Alexey Kolydenko, Bai Li
    Subjects: Methodology
    Abstract

    The statistical analysis of covariance matrices occurs in many important
    applications, e.g. in diffusion tensor imaging and longitudinal data analysis.
    We consider the situation where it is of interest to estimate an average
    covariance matrix, describe its anisotropy, to carry out principal geodesic
    analysis and to interpolate between covariance matrices. There are many choices
    of metric available, each with its advantages. The particular choice of what is
    best will depend on the particular application.

  2. Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging.

    Пнд, 10/12/2009 - 10:31 — SomNambul
    Authors: Ian L. Dryden, Alexey Koloydenko, Diwei Zhou
    Subjects: Applications
    Abstract

    The statistical analysis of covariance matrix data is considered and, in
    particular, methodology is discussed which takes into account the non-Euclidean
    nature of the space of positive semi-definite symmetric matrices. The main
    motivation for the work is the analysis of diffusion tensors in medical image
    analysis. The primary focus is on estimation of a mean covariance matrix and,
    in particular, on the use of Procrustes size-and-shape space. Comparisons are
    made with other estimation techniques, including using the matrix logarithm,
    matrix square root and Cholesky decomposition.

  3. Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging.

    Пнд, 10/12/2009 - 10:31 — SomNambul
    Authors: Ian L. Dryden, Alexey Koloydenko, Diwei Zhou
    Subjects: Applications
    Abstract

    The statistical analysis of covariance matrix data is considered and, in
    particular, methodology is discussed which takes into account the non-Euclidean
    nature of the space of positive semi-definite symmetric matrices. The main
    motivation for the work is the analysis of diffusion tensors in medical image
    analysis. The primary focus is on estimation of a mean covariance matrix and,
    in particular, on the use of Procrustes size-and-shape space. Comparisons are
    made with other estimation techniques, including using the matrix logarithm,
    matrix square root and Cholesky decomposition.

Math-arch

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