General framework for asymptotic properties of generalized weighted nonlinear least-squares estimators with deterministic and stochastic weighting

This paper studies the asymptotic properties (strong consistency, convergence rate, asymptotic normality) of a generalized weighted nonlinear least-squares estimator under weak noise assumptions. Both deterministic and stochastic weighting are handled and the presence of model errors is considered. For particular models, estimators, and noise assumptions the general framework boils down to known time and frequency-domain estimators.