Bayesian Approaches to Nonparametric Estimation of Densities on the Unit Interval

Song Li, Mervyn J. Silvapulle, Param Silvapulle and Xibin Zhang ()

Econometric Reviews, 2015, vol. 34, issue 3, 394-412

Abstract: 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.

Date: 2015
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Working Paper: Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval (2012)
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DOI: 10.1080/07474938.2013.807130

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