Bayesian Approaches to Nonparametric Estimation of Densities on the Unit Interval
研究了单位区间[0,1]上密度的非参数估计，提出一个统一的贝叶斯框架来选择带宽和核函数，模拟和实证表明该方法能改进现有估计方式。
This paper investigates nonparametric estimation of density on [0, 1]. The kernel estimator of density on [0, 1] has been found to be sensitive to both bandwidth and kernel. This paper proposes a unified Bayesian framework for choosing both the bandwidth and kernel function. In a simulation study, the Bayesian bandwidth estimator performed better than others, and kernel estimators were sensitive to the choice of the kernel and the shapes of the population densities on [0, 1]. The simulation and empirical results demonstrate that the methods proposed in this paper can improve the way the probability densities on [0, 1] are presently estimated.