Bissantz, NicolaiDette, HolgerProksch, Katharina2009-10-292009-10-292009-10-16http://hdl.handle.net/2003/2650110.17877/DE290R-751We consider the problem of testing parametric assumptions in an inverse regression model with a convolution-type operator. An L^2-type goodness-of-fit test is proposed which compares the distance between a parametric and a nonparametric estimate of the regression function. Asymptotic normality of the corresponding test statistic is shown under the null hypothesis and under a general nonparametric alternative with different rates of convergence in both cases. The feasibility of the proposed test is demonstrated by means of a small simulation study. In particular, the power of the test against certain types of alternative is investigated.enDiscussion Paper / SFB 823; 29/2009goodness-of-fit testimit theorems for quadratic formsmodel selectionnverse problems310330620report