402 citations · 543 across the 18 of their papers we have counts for
9 papers · 1 filter
Principal Nested Cones
Yanyan Zhan, Ian L. Dryden, Yuexuan Wu
In many applications, the data lie on a type of cone, where there is a distinction between an overall scale variable and the remaining scale-free structure. For example, the joint…
Non-parametric regression for networks
Katie E. Severn, Ian L. Dryden, Simon P. Preston
Network data are becoming increasingly available, and so there is a need to develop suitable methodology for statistical analysis. Networks can be represented as graph Laplacian ma…
Principal nested shape space analysis of molecular dynamics data
Ian L. Dryden, Kwang-Rae Kim, Charles A. Laughton +1
Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of interest to summarize the shape evolution of the molecules in a succinct, low-dime…
Manifold valued data analysis of samples of networks, with applications in corpus linguistics
Katie E. Severn, Ian L. Dryden, Simon P. Preston
Networks arise in many applications, such as in the analysis of text documents, social interactions and brain activity. We develop a general framework for extrinsic statistical ana…
Smoothing splines on Riemannian manifolds, with applications to 3D shape space
Kwang-Rae Kim, Ian L. Dryden, Huiling Le +1
There has been increasing interest in statistical analysis of data lying in manifolds. This paper generalizes a smoothing spline fitting method to Riemannian manifold data based on…
Comparing Trajectories on the Size and Shape Space
Valerio Varano, Stefano Gabriele, Luciano Teresi +4
In this paper we show that trajectory shape analysis should be performed only after obtaining a proper representation before applying ordination methods. In fact, studying the shap…