Regression and curve fitting for networks and shapes

  • Ian Dryden (Florida International University)
E1 05 (Leibniz-Saal)


Complex object data such as networks and shapes are becoming increasingly available, and so there is a need to develop suitable methodology for statistical analysis. Networks can be represented as graph Laplacian matrices, which are a type of manifold-valued data. Shapes of 3D objects are also a type ofmanifold-valued data, invariant to translation, rotation and scale. Our main objective is to estimate a regression curve from a sample of graph Laplacian matrices or 3D shapes conditional on a set of Euclidean covariates, for example in dynamic objects where the covariate is time. We develop an adapted Nadaraya-Watson estimator which has uniform weak consistency for estimation using Euclidean and power Euclidean metrics, and we also explore splines on shape spaces.

Katharina Matschke

Max Planck Institute for Mathematics in the Sciences, Leipzig Contact via Mail

Karen Habermann

University of Warwick

Sayan Mukherjee

Max Planck Institute for Mathematics in the Sciences, Leipzig

Max von Renesse

Leipzig University

Stefan Horst Sommer

University of Copenhagen