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Asymptotic normality of the continuous-time stochastic approximation algorithm

Asymptotic normality of the continuous-time stochastic approximation algorithm In this paper the continuous-time stochastic approximation algorithm seeking for the zero of a regression function is considered when the measurement error is a stochastic process generated by an Ito integral as the input of a linear system. The conditions are given to guarantee the asymptotic normality of the algorithm which is modified from the Robbins-Monro procedure proposed for the case where the measurement error is a process of independent increment. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

Asymptotic normality of the continuous-time stochastic approximation algorithm

Hanfu, Chen
Acta Mathematicae Applicatae Sinica , Volume 1 (1) – Jun 15, 2005
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References (9)

Publisher
Springer Journals
Copyright
Copyright © 1984 by Science Press
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/BF01883891
Publisher site
See Article on Publisher Site

Abstract

In this paper the continuous-time stochastic approximation algorithm seeking for the zero of a regression function is considered when the measurement error is a stochastic process generated by an Ito integral as the input of a linear system. The conditions are given to guarantee the asymptotic normality of the algorithm which is modified from the Robbins-Monro procedure proposed for the case where the measurement error is a process of independent increment.

Journal

Acta Mathematicae Applicatae Sinica – Springer Journals

Published: Jun 15, 2005

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