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Other Publications Year : 2010

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

Abstract

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.

Dates and versions

hal-00813769 , version 1 (02-05-2013)

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I. L. Dryden, Xavier Pennec, Jean-Marc Peyrat. Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging. 2010. ⟨hal-00813769⟩
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