Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval
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.
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