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TitleEstimation of directional wind response using noisy bivariate circular data: a comparison of approaches
PresenterRachael Quill (The University of Adelaide)
Author(s)Rachael Quill
SessionStatistics
Time15:00:00 2017-09-25
Abstract


Observed bivariate datasets are considered noisy realisations of true underlying
continuous signals to be estimated. Despite recent developments in toroidal
kernel density estimation, there are limited alternatives to account for
toroidality in nonparametric smoothing techniques. A simple and intuitive
strategy to handle circular data is to wrap the dataset and reveal its circular
nature prior to estimation. Traditional planar estimation techniques can then be
utilised. This study aims to analyse this simple strategy in comparison to
explicitly toroidal techniques. It is shown that using such an approach can
produce as good, if not better surface estimation results than these toroidal
methods. However, results are dependent upon the choice of planar estimation
technique, and trade-offs between accuracy, smoothness and computational demand
must be made in practice. Lessons from a simulation study are used in the
estimation of bivariate wind direction distributions to understand the response
of prevailing wind over mountainous terrain.