Effect of measurement error on the shape of the regression function in nonlinear models

In this thesis we study the effect of regressors measured with an error on an estimated coefficients in a generalized linear model. We infer the true shape of the mean and of the variance function in the given model. We show that assumptions of a generalized linear model are not fulfilled universally if we use variables measured with an error. Despite this, the error-in-variable model can still be useful for testing dependence of original correct regressor. Further on in the thesis, the asymptotic values of coefficients are approximated, assuming g(E(Yi|Wi)) is a quadratic function. Examples for all results are provided through simulations