Bayesian Approaches to Nonparametric Estimation of Densities on the Unit Interval |
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Authors: | Song Li Mervyn J. Silvapulle Xibin Zhang |
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Affiliation: | Department of Econometrics and Business Statistics , Monash University , Victoria , Australia |
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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. |
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Keywords: | Asymmetric kernel Bayes factor Boundary bias Kernel selection Marginal likelihood Recovery-rate density |
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