STOCHASTIC APPROXIMATION PROCEDURES WITH RANDOMLY VARYING TRUNCATIONS

In this paper, we propose a new procedure with randomly varying truncations to search for zeros or extreme of a regression function. Under quite weak conditions imposed on the regression function, we prove the global convergence of the procedure when the measurement errors belong to a class of dependent random sequences including the stationary ARMA(Autoregressive and Moving Average) process as its special case.