Asymptotic properties of parallel Bayesian kernel density estimators
Statistics Theory
2020-11-09 v2 Statistics Theory
Abstract
In this article we perform an asymptotic analysis of Bayesian parallel kernel density estimators introduced by Neiswanger, Wang and Xing (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.
Cite
@article{arxiv.1611.02874,
title = {Asymptotic properties of parallel Bayesian kernel density estimators},
author = {Alexey Miroshnikov and Evgeny Savelev},
journal= {arXiv preprint arXiv:1611.02874},
year = {2020}
}
Comments
31 pages, 8 pictures, submitted