Asymptotic properties of parallel Bayesian kernel density estimators

Alexey Miroshnikov () and Evgeny Savelev ()
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Alexey Miroshnikov: University of California
Evgeny Savelev: Virginia Polytechnic Institute and State University

Annals of the Institute of Statistical Mathematics, 2019, vol. 71, issue 4, No 3, 810 pages

Abstract: Abstract In this article, we perform an asymptotic analysis of Bayesian parallel kernel density estimators introduced by Neiswanger et al. (in: Proceedings of the thirtieth conference on uncertainty in artificial intelligence, AUAI Press, pp 623–632, 2014). We derive the asymptotic expansion of the mean integrated squared error for the full data posterior estimator and investigate the properties of asymptotically optimal bandwidth parameters. Our analysis demonstrates that partitioning data into subsets requires a non-trivial choice of bandwidth parameters that optimizes the estimation error.

Keywords: Density estimation; Asymptotic properties; Parametric optimization; Parallel algorithms (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10463-018-0662-0

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