Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/36606 
Year of Publication: 
2008
Series/Report no.: 
Technical Report No. 2008,16
Publisher: 
Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Abstract: 
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two period functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt [Ann. Statist. 1, 10711095] we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract.
Subjects: 
Confidencebands
Inverseproblems
Deconvolution
Rates of convergences
Nonparametric Regression
Document Type: 
Working Paper

Files in This Item:
File
Size
317.83 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.