asymptotically efficient and also useful in the cases when there may not exist a two-sided asymptotically efficient estimator since we may find an AMU estimator whose asymptotic distribution attains at least at a point, or an AMU estimator whose asymptotic distribution is uniformly "close" to it....
The best estimator is called projective, as it is defined by taking certain projections of scoring functions. In the special case of fully (or partially) specified models it coincides asymptotically with the maximum (partial) likelihood estimator, in signal plus noise models with the Gauss-Markov ...