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Convergence of Stochastic approximation algorithm under irregular conditions

Zhang, Jian, 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: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) (KAR id:41700)

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http://dx.doi.org/10.1111/j.1467-9574.2008.00397.x

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
DOI/Identification number: 10.1111/j.1467-9574.2008.00397.x
Uncontrolled keywords: Stochastic approximation algorithm; M-estimator; Exponential power mixture models.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jian Zhang
Date Deposited: 07 Jul 2014 13:47 UTC
Last Modified: 16 Nov 2021 10:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41700 (The current URI for this page, for reference purposes)

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