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Stochastic approximation with non-additive measurement noise

Part of: Sequential methods

Published online by Cambridge University Press:  14 July 2016

Han-Fu Chen
Han-Fu Chen*
Affiliation:
The Chinese Academy of Sciences
*
∗Postal address: Laboratory of Systems & Control, Institute of Systems Science, The Chinese Academy of Sciences, Beijing 100080, China. E-mail address: hfchen@iss03.iss.ac.cn
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Abstract

The Robbins–Monro algorithm with randomly varying truncations for measurements with non-additive noise is considered. Assuming that the function under observation is locally Lipschitz-continuous in its first argument and that the noise is a φ-mixing process, strong consistency of the estimate is shown. Neither growth rate restriction on the function, nor the decreasing rate of the mixing coefficients are required.

Keywords

Stochastic approximationnon-additive noiserandomly varying truncation

MSC classification

Primary: 62L20: Stochastic approximation
Type
Research Papers
Information
Journal of Applied Probability , Volume 35 , Issue 2 , June 1998 , pp. 407 - 417
Copyright
Copyright © Applied Probability Trust 1998 

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Footnotes

Supported by the National Climbing Project of China and the National Natural Science Foundation of China.

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