A stopping rule for the Robbins-Monro method
A stopping rule for the multi-dimensional Robbins-Monro stochastic approximation method is developed. Both moving average and stationary \(\phi\)-mixing type of correlated noise processes are treated. Sequentially determined confidence ellipsoids are constructed to fulfill the goal for the determination of the stopping rule. The limit behavior of the algorithm is investigated. It is shown that the stopped Robbins- Monro process is asymptotically normal. Such asymptotic normality is established by means of weak convergence methods.
sequentially determined confidence ellipsoids
scientific article; zbMATH DE number 4126525
scientific article