A Bayesian Stochastic Approximation Method
Methodology
2017-05-08 v1
Abstract
Motivated by the goal of improving the efficiency of small sample design, we propose a novel Bayesian stochastic approximation method to estimate the root of a regression function. The method features adaptive local modelling and nonrecursive iteration. Strong consistency of the Bayes estimator is obtained. Simulation studies show that our method is superior in finite-sample performance to Robbins--Monro type procedures. Extensions to searching for extrema and a version of generalized multivariate quantile are presented.
Cite
@article{arxiv.1705.02069,
title = {A Bayesian Stochastic Approximation Method},
author = {Jin Xu and Cui Xiong and Rongji Mu},
journal= {arXiv preprint arXiv:1705.02069},
year = {2017}
}