  This paper introduces and analyzes a stochastic search method for parameter
estimation in linear regression models in the spirit of Beran and Millar
(1987). The idea is to generate a random finite subset of a parameter space
which will automatically contain points which are very close to an unknown true
parameter. The motivation for this procedure comes from recent work of
Duembgen, Samworth and Schuhmacher (2011) on regression models with log-concave
error distributions.
