Monte Carlo Study of Three Data-Based Nonparametric Probability Density Estimators
本文回顾并评估了三种基于数据的算法，这些算法能完全从随机样本中确定密度估计。通过蒙特卡洛模拟，比较了它们的统计精度（用积分均方误差衡量），并考察了对异常值的敏感性和计算时间。
Abstract 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 these algorithms as measured by the integrated mean squared error. In addition, we examine the sensitivity of these algorithms to outliers and estimate computer time requirements. One conclusion we draw is that the statistical accuracy of these data-based algorithms seems comparable to levels predicted by theoretical models.