Estimating the endpoint of a distribution in the presence of additive observation errors
We consider the problem of estimating the endpoint of a probability distribution in the presence of observation errors, when the available sample is drawn from the convolution with some error density. We study the cases of Gaussian errors and errors with bounded support, and propose estimators that are optimal in a minimax sense.
Volume (Year): 68 (2004)
Issue (Month): 1 (June)
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