Estimation of densities and derivatives of densities with directional data
Journal of Multivariate Analysis
Robust estimation for circular data
Computational Statistics & Data Analysis
A plug-in rule for bandwidth selection in circular density estimation
Computational Statistics & Data Analysis
(OBIFS) isotropic image analysis for improving a predicting agent based systems
Expert Systems with Applications: An International Journal
Kernel density estimation for directional-linear data
Journal of Multivariate Analysis
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Given angular data @q"1,...,@q"n@?[0,2@p) a common objective is to estimate the density. In case that a kernel estimator is used, bandwidth selection is crucial to the performance. A ''plug-in rule'' for the bandwidth, which is based on the concentration of a reference density, namely, the von Mises distribution is obtained. It is seen that this is equivalent to the usual Euclidean plug-in rule in the case where the concentration becomes large. In case that the concentration parameter is unknown, alternative methods are explored which are intended to be robust to departures from the reference density. Simulations indicate that ''wrapped estimators'' can perform well in this context. The methods are applied to a real bivariate dataset concerning protein structure.