Abstract
In this paper we consider a class of stochastic approximation procedures that arises in linear filtering and regression theory. Our main result asserts that the stochastic approximation process satisfies an almost sure invariance principle (with a certain rate of convergence) if the partial sums of the errors do.
Citation
Erich Berger. "An almost sure invariance principle for stochastic approximation procedures in linear filtering theory." Ann. Appl. Probab. 7 (2) 444 - 459, May 1997. https://doi.org/10.1214/aoap/1034625339
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