Bayesian approaches to nonparametric estimation of densities on the unit interval

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)394 - 412
Number of pages19
JournalEconometric Reviews
Issue number3
Publication statusPublished - 2015

Cite this