Stochastic approximation with non-additive measurement noise

Author:

Chen Han-Fu

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.

Publisher

Cambridge University Press (CUP)

Subject

Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability

Reference11 articles.

1. Almost sure convergence of stochastic approximation algorithms with non-additive noise

2. [9] Tadić V. Stochastic optimization by a gradient algorithm with random truncations. Preprint.

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4. [1] Chen H.F. (1994). Stochastic approximation and its new applications. In Proc. 1994 Hong Kong International Workshop on New Directions of Control and Manufacturing, 2–12.

5. Stochastic approximation procedure with randomly varying truncations;Chen;Scientia Sinica (series A),1986

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