A stochastic approximation-iterative least squares estimation procedure

Abstract EN

We consider the general nonlinear regression problem.

A survey of some classical methods and stochastic approximation procedures for estimating is first given.

We solve the nonlinear regression problem by considering the optimal stochastic approximation procedure by [3], [4].

This leads us to introduce a new procedure, called "Stochastic Approximation Iterative Least Square Procedure" SA -ILS procedure.

The new procedure is applied to a number of nonlinear regression models.

We report on the results of a simulation investigation which indicate that the new procedure is highly efficient with respect to the number of observations required to obtain the parameter estimates for given regression problem.