Functional additive regression on shape and form manifolds of planar curves

Defining shape and form as equivalence classes under translation, rotation and -- for shapes -- also scale, we extend generalized additive regression to models for the shape/form of planar curves or landmark configurations. The model respects the resulting quotient geometry of the response, employing the squared geodesic distance as loss function and a geodesic response function mapping the additive predictor to the shape/form space. For fitting the model, we propose a Riemannian $L_2$-Boosting algorithm well-suited for a potentially large number of possibly parameter-intensive model terms, which also yiels automated model selection. We provide novel intuitively interpretable visualizations for (even non-linear) covariate effects in the shape/form space via suitable tensor based factorizations. The usefulness of the proposed framework is illustrated in an analysis of 1) astragalus shapes of wild and domesticated sheep and 2) cell forms generated in a biophysical model, as well as 3) in a realistic simulation study with response shapes and forms motivated from a dataset on bottle outlines.