Issue No. 04 - April (2010 vol. 32)
Stephan Huckemann , Georgia Augusta University, Göttingen
Thomas Hotz , Georgia Augusta University, Göttingen
Axel Munk , Georgia Augusta University, Göttingen
We propose an intrinsic multifactorial model for data on Riemannian manifolds that typically occur in the statistical analysis of shape. Due to the lack of a linear structure, linear models cannot be defined in general; to date only one-way MANOVA is available. For a general multifactorial model, we assume that variation not explained by the model is concentrated near elements defining the effects. By determining the asymptotic distributions of respective sample covariances under parallel transport, we show that they can be compared by standard MANOVA. Often in applications manifolds are only implicitly given as quotients, where the bottom space parallel transport can be expressed through a differential equation. For Kendall's space of planar shapes, we provide an explicit solution. We illustrate our method by an intrinsic two-way MANOVA for a set of leaf shapes. While biologists can identify genotype effects by sight, we can detect height effects that are otherwise not identifiable.
Shape analysis, nonlinear multivariate analysis of variance, Riemannian manifolds, orbifolds, orbit spaces, geodesics, Lie group actions, nonlinear multivariate statistics, covariance, inference, test, intrinsic mean, forest biometry.
A. Munk, T. Hotz and S. Huckemann, "Intrinsic MANOVA for Riemannian Manifolds with an Application to Kendall's Space of Planar Shapes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 593-603, 2009.