Abstract
This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar [ Ann. Statist. 15(3) (1987) 1131–1154]. 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 Dümbgen et al. [ Ann. Statist. 39(2) (2011) 702–730] on regression models with log-concave error distributions.
Information
Digital Object Identifier: 10.1214/12-IMSCOLL907