Bootstrap Confidence Cones for Directional Data
针对p维球面上的方向数据，提出基于自助法的非参数置信锥，并在最小条件下证明其一致性，通过蒙特卡洛模拟比较了不同方法的性能。
Given a sample from a distribution F supported on a p-dimensional sphere, several confidence cones for the ‘mean directional vector’ are proposed. Some methods have been available, but they depend on parametric assumptions. In this paper, nonparametric confidence cones based on the bootstrap are considered, and consistency of these methods is obtained under minimal conditions. We compare the performances of bootstrap methods with the others by Monte Carlo simulations.