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.
- Estimation of support of a probability density
- Extreme value distribution
- Optimal rates of convergence
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty