Monte Carlo Study of Three Data-Based Nonparametric Probability Density Estimators
三种数据驱动非参数概率密度估计方法的蒙特卡洛研究
Although the theoretical properties of modern nonparametric probability density estimators have been studied for 25 years, there remains the practical problem of how to specify the amount of bias or smoothing in a density estimate based on a random sample. In this paper we review and evaluate three recently developed data-based algorithms that completely specify a density estimate from a random sample. Using Monte Carlo techniques, we compare the statistical accuracy of…