Convergence of Stochastic approximation algorithm under irregular conditions

Zhang, Jian and Liang, Faming (2008) Convergence of Stochastic approximation algorithm under irregular conditions. Statistica Neerlandica, 62 (3). pp. 393-403. ISSN 0039-0402. E-ISSN 1467-9574. (doi:https://doi.org/10.1111/j.1467-9574.2008.00397.x) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

We consider a class of stochastic approximation (SA) algorithms for solving a system of estimating equations. The standard condition for the convergence of the SA algorithms is that the estimating functions are locally Lipschitz continuous. Here, we show that this condition can be relaxed to the extent that the estimating functions are bounded and continuous almost everywhere. As a consequence, the use of the SA algorithm can be extended to some problems with irregular estimating functions. Our theoretical results are illustrated by solving an estimation problem for exponential power mixture models.

Item Type: Article
Uncontrolled keywords: Stochastic approximation algorithm; M-estimator; Exponential power mixture models.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 07 Jul 2014 13:47 UTC
Last Modified: 13 Feb 2015 15:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41700 (The current URI for this page, for reference purposes)
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