Limit theorems for stochastic approximation algorithms.
2011 (English)Report (Other academic)
We prove a central limit theorem applicable to one dimensional stochastic approximation algorithms that converge to a point where the error terms of the algorithm do not vanish. We show how this applies to a certain class of these algorithms that in particular covers a generalized Pólya urn model, which is also discussed. In addition, we show how to scale these algorithms in some cases where we cannot determine the limiting distribution but expect it to be non-normal.
Place, publisher, year, edition, pages
2011. , 26 p.
, U.U.D.M, 2011:6
stochastic approximation algorithms, central limit theorem, generalized Polya urn
Probability Theory and Statistics
Research subject Mathematical Statistics
IdentifiersURN: urn:nbn:se:uu:diva-145343OAI: oai:DiVA.org:uu-145343DiVA: diva2:396177