- Volume 25, Number 2 (2019), 1141-1159.
Convergence rates for a class of estimators based on Stein’s method
Gradient information on the sampling distribution can be used to reduce the variance of Monte Carlo estimators via Stein’s method. An important application is that of estimating an expectation of a test function along the sample path of a Markov chain, where gradient information enables convergence rate improvement at the cost of a linear system which must be solved. The contribution of this paper is to establish theoretical bounds on convergence rates for a class of estimators based on Stein’s method. Our analysis accounts for (i) the degree of smoothness of the sampling distribution and test function, (ii) the dimension of the state space, and (iii) the case of non-independent samples arising from a Markov chain. These results provide insight into the rapid convergence of gradient-based estimators observed for low-dimensional problems, as well as clarifying a curse-of-dimension that appears inherent to such methods.
Bernoulli, Volume 25, Number 2 (2019), 1141-1159.
Received: March 2017
Revised: August 2017
First available in Project Euclid: 6 March 2019
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Digital Object Identifier
Oates, Chris J.; Cockayne, Jon; Briol, François-Xavier; Girolami, Mark. Convergence rates for a class of estimators based on Stein’s method. Bernoulli 25 (2019), no. 2, 1141--1159. doi:10.3150/17-BEJ1016. https://projecteuclid.org/euclid.bj/1551862846
- Supplement to “Convergence rates for a class of estimators based on Stein’s method”. Proofs of all theoretical results are provided.