Asymptotic normality of one-step M-estimators based on non-identically distributed observations

We find general conditions for asymptotic normality of two types of one-step M-estimators based on independent not necessarily identically distributed observations. As an application, we consider some examples of one-step approximation of quasi-likelihood estimators in nonlinear regression.