Confidence bands for inverse regression models with application to gel electrophoresis
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Date
2008-11-26T14:50:14Z
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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.
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Keywords
Confidence band, Deconvolution, Inverse problem, Nonparametric regression, Rate of convergence