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Testing for lack of fit in inverse regression - with applications to photonic imaging.

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  • Mathematics
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Abstract

Testing for lack of fit in inverse regression – with applications to photonic imaging Nicolai Bissantz, Gerda Claeskens, Hajo Holzmann and Axel Munk DEPARTMENT OF DECISION SCIENCES AND INFORMATION MANAGEMENT (KBI) Faculty of Economics and Applied Economics KBI 0714 Testing for lack of fit in inverse regression – with applications to photonic imaging Nicolai Bissantz1, Gerda Claeskens2, Hajo Holzmann3 and Axel Munk3 1Lehrstuhl fu¨r Stochastik Ruhr-Universita¨t Bochum, Germany 2ORStat & University Center for Statistics K.U.Leuven, Belgium 3Institut fu¨r Mathematische Stochastik Georg-August-University at Go¨ttingen, Germany March 1, 2007 Abstract We propose two test statistics for use in inverse regression problems Y = Kθ + �, where K is a given matrix or operator which cannot be continuously inverted. Thus, only noisy, indirect observations Y for the function θ are available. The tests are designed for hypotheses of the form H0 : θ(x) = ∑p j=1 ajφj(x), where (φj)j≥1 is the orthonormal system of basis functions given by the spectral decomposition of the operator K. Both test statistics have a counterpart in classical hypothesis testing, where they are called the order selection test and the data-driven Neyman smooth test. We also introduce two model selection criteria which extend the classical AIC and BIC to inverse regression problems. In a simulation study we show that the inverse order selection and Neyman smooth tests outperform their direct counterparts in many cases. The theory is motivated by data arising in confocal fluorescence microscopy. Here, images are observed with blurring (modeled as deconvolution) and stochastic error at subsequent times. The aim is then to reduce the signal to noise ratio by averaging over the distinct images. In this context it is relevant to decide whether the images are still equal (or have changed by outside influences such as moving of the object table). Keywords: Hypothesis testing, Inverse problems, Model selection, Nanosc

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