Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging

Details Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging

2010-09-15

arxiv.org/abs/1009.3045v1

Statistics

Methodology

Scientific paper

Various metrics for comparing diffusion tensors have been recently proposed in the literature. We consider a broad family of metrics which is indexed by a single power parameter. A likelihood-based procedure is developed for choosing the most appropriate metric from the family for a given dataset at hand. The approach is analogous to using the Box-Cox transformation that is frequently investigated in regression analysis. The methodology is illustrated with a simulation study and an application to a real dataset of diffusion tensor images of canine hearts.

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