Nonparametric smoothing of directional and axial data

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• Lutz Duembgen (Universität Bern)
• Thursday 10 July 2025, 11:00-12:00
• Seminar Room 2, Newton Institute.

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RCL - Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning

We discuss generalized linear models for directional data where the conditional distribution of the response is a von Mises-Fisher distribution in arbitrary dimension or a Bingham distribution on the unit circle. To do this properly, we parametrize von Mises-Fisher distributions by Euclidean parameters and investigate computational aspects of this parametrization. Then we modify this approach for local polynomial regression as a means of nonparametric smoothing of distributional data. The methods are illustrated with simulated data and a data set from planetary sciences involving covariate vectors on a sphere with axial response. This is joint work with Caroline Haslebacher (SWRI, Boulder, USA ).

This talk is part of the Isaac Newton Institute Seminar Series series.

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