Estimating a Changepoint, Boundary of Frontier in the Presence of Observation Error
When stochastic errors are added to data from a distribution with a sharp boundary, such as a changepoint or a frontier, nonparametric estimation of the boundary can be interpreted as a problem of deconvolution. We argue that, rather than attempting to estimate the distribution of the uncorrupted data, and thereby approximate the boundary, one might focus more directly on the boundary estimation problem.
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