Abstract:
Density estimation represents a core tool in statistics for both exploring data structures and as a starting task in more challenging problems. We consider nonparametric estimation of circular densities, which are periodic probability density functions having the unit circle as their support. Starting from the basic idea of kernel estimation of circular densities, we discuss some related methods and present some adaptations to error-in-variables problems.