Limit theorems for stochastic approximation algorithms.

• 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.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

stochastic approximation algorithms

central limit theorem

generalized Polya urn

Mathematical statistics

Matematisk statistik

Mathematical Statistics

Matematisk statistik

Publikations- och innehållstyp

vet (ämneskategori)

rap (ämneskategori)