Authors: Bissantz, Nicolai
Dette, Holger
Proksch, Katharina
Title: Model checks in inverse regression models with convolution-type operators
Language (ISO): en
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
model selection
nverse problems
Issue Date: 2009-10-16
Appears in Collections:Sonderforschungsbereich (SFB) 823

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