Making a Non-Parametric Density Estimator More Attractive, and More Accurate, by Data Perturbation
通过数据扰动改善非参数密度估计的形态与精度
Summary Motivated by both the shortcomings of high order density estimators, and the increasingly large data sets in many areas of modern science, we introduce new high order, non-parametric density estimators that are guaranteed to be positive and do not have highly oscillatory tails. Our approach is based on data perturbation, e.g. by tilting or data sharpening. It leads to new estimators that are more accurate than conventional kernel techniques that use positive kernels,…