  This paper is concerned with general nonlinear regression models where the
predictor variables are subject to Berkson-type measurement errors. The
measurement errors are assumed to have a general parametric distribution, which
is not necessarily normal. In addition, the distribution of the random error in
the regression equation is nonparametric. A minimum distance estimator is
proposed, which is based on the first two conditional moments of the response
variable given the observed predictor variables. To overcome the possible
computational difficulty of minimizing an objective function which involves
multiple integrals, a simulation-based estimator is constructed. Consistency
and asymptotic normality for both estimators are derived under fairly general
regularity conditions.
