Optimal change-point estimation in inverse problems

Michael H. Neumann
We develop a method of estimating change-points of a function in the case of indirect noisy observations. As two paradigmatic problems we consider deconvolution and errors-in-variables regression. We estimate the scalar products of our indirectly observed function with appropriate test functions, which are shifted over the interval of interest. An estimator of the change point is obtained by the extremal point of this quantity. We derive rates of convergence for this estimator. They depend on...
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