  New bandwidth selectors for kernel density estimation with directional data
are presented in this work. These selectors are based on asymptotic and exact
error expressions for the kernel density estimator combined with mixtures of
von Mises distributions. The performance of the proposed selectors is
investigated in a simulation study and compared with other existing rules for a
large variety of directional scenarios, sample sizes and dimensions. The
selector based on the exact error expression turns out to have the best
behaviour of the studied selectors for almost all the situations. This selector
is illustrated with real data for the circular and spherical cases.
