![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Nonparametric smoothing of directional and axial data
![]() Nonparametric smoothing of directional and axial dataAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact nobody. 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. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsMadingley Lunchtime Seminars Talking to Tyrants Book Launch Nursing Essay Writing ServiceOther talksLunch and Finish Elevator Pitch Talk 6 Break Research Discussion On Selection Bias and Fairness Issues in Machine Learning Topological Invariants for G-kernels and Group Actions |