Lower bias circular density estimation with contaminated data

We study the problem of estimating circular densities when
sample data are affected by measurement errors. We propose a deconvolution
approach involving lower bias kernel estimators which take the
additional source of bias due to the presence of measurement errors into
account. Some asymptotic properties are discussed, and numerical results
are provided.