|Title:||Model checks in inverse regression models with convolution-type operators|
|Abstract:||We 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.|
|Subject Headings:||goodness-of-fit test|
imit theorems for quadratic forms
|Appears in Collections:||Sonderforschungsbereich (SFB) 823|
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