  A generic, fast and asymptotically efficient method for parametric estimation
is described. It is based on the projected stochastic gradient descent on the
log-likelihood function corrected by a single step of the Fisher scoring
algorithm. We show theoretically and by simulations that it is an interesting
alternative to the usual stochastic gradient descent with averaging or the
adaptative stochastic gradient descent.
