On the consistency of a class of nonlinear regression estimators

In this paper,we study conditions sufficient for strong consistency of a class of estimators of parameters of nonlinear regression models. the study considers continuous functions depending on a vector of parameters and a set of random regressors. the estimators chosen are minimizers of a generalized form of the signed-rank norm. the generalization allows us to make consistency statements about minimizers of a wide variety of norms including the l 1 and l 2 norms. by implementing trimming,it is shown that high breakdown estimates can be obtained based on the proposed dispersion function.