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Bayesian Variance-Stabilizing Kernel Density Estimation Using Conjugate Prior

  • K. NishidaEmail author
Article

Kernel-type density or regression estimator does not produce a constant estimator variance over the domain. To correct this problem, K. Nishida and Y. Kanazawa (2011, 2015) proposed a variance-stabilizing (VS) local variable bandwidth for kernel regression estimators. K. Nishida (2017) proposed another strategy to construct VS local linear regression estimator using a convex combination of three skewing estimators proposed by Choi and Hall (1998). In this study, we show that variance stabilization can be accomplished by a Bayesian approach in the case of kernel density estimator using conjugate prior.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.General Education CenterHyogo University of Health SciencesKobeJapan

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