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A Bayesian approach to statistical shape analysis via the projected normal distribution. (English) Zbl 1416.62371

Summary: This work presents a Bayesian predictive approach to statistical shape analysis. A modeling strategy that starts with a Gaussian distribution on the configuration space, and then removes the effects of location, rotation and scale, is studied. This boils down to an application of the projected normal distribution to model the configurations in the shape space, which together with certain identifiability constraints, facilitates parameter interpretation. Having better control over the parameters allows us to generalize the model to a regression setting where the effect of predictors on shapes can be considered. The methodology is illustrated and tested using both simulated scenarios and a real data set concerning eight anatomical landmarks on a sagittal plane of the corpus callosum in patients with autism and in a group of controls.

MSC:

62H35 Image analysis in multivariate analysis
62F15 Bayesian inference
62J05 Linear regression; mixed models
62P10 Applications of statistics to biology and medical sciences; meta analysis
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References:

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