This paper discusses the solution of nonlinear integral equations with noisy
integral kernels as they appear in nonparametric instrumental regression. We
propose a regularized Newton-type iteration and establish convergence and
convergence rate results. A particular emphasis is on instrumental regression
models where the usual conditional mean assumption is replaced by a stronger
independence assumption. We demonstrate for the case of a binary instrument
that our approach allows the correct estimation of regression functions which
are not identifiable with the standard model. This is illustrated in computed
examples with simulated data