Authors: | Birke, Melanie Bissantz, Nicolai Holzmann, Hajo |
Title: | Confidence bands for inverse regression models with application to gel electrophoresis |
Language (ISO): | en |
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, 1071–1095] 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. |
Subject Headings: | Confidence band Deconvolution Inverse problem Nonparametric regression Rate of convergence |
URI: | http://hdl.handle.net/2003/25879 http://dx.doi.org/10.17877/DE290R-14439 |
Issue Date: | 2008-11-26T14:50:14Z |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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tr16-08-Birke.pdf | DNB | 317.83 kB | Adobe PDF | View/Open |
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