Let X1, ..., Xn be independent identically distributed observations from an unknown probability density f(·), such that its support G = supp f is a subset of the unit square in 2. We consider the problem of estimating G from the sample X1, ..., Xn, under the assumption that the boundary of G...

The problem of optimal prediction in the stochastic linear regression model with infinitely many parameters is considered. We suggest a prediction method that outperforms asymptotically the ordinary least squares predictor. Moreover, if the random errors are Gaussian, the method is...

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...