Truncated Stochastic Approximation with Moving Bounds: Convergence
Methodology
2012-05-04 v4 Probability
Abstract
In this paper we propose a wide class of truncated stochastic approximation procedures with moving random bounds. While we believe that the proposed class of procedures will find its way to a wider range of applications, the main motivation is to accommodate applications to parametric statistical estimation theory. Our class of stochastic approximation procedures has three main characteristics: truncations with random moving bounds, a matrix valued random step-size sequence, and dynamically changing random regression function. We establish convergence and consider several examples to illustrate the results.
Cite
@article{arxiv.1101.0031,
title = {Truncated Stochastic Approximation with Moving Bounds: Convergence},
author = {Teo Sharia},
journal= {arXiv preprint arXiv:1101.0031},
year = {2012}
}