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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Official URL: 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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