Abstract:
We consider the problem of estimating an unknown vector observed in a simple white Gaussian noise model. For the estimation, a family of projection estimators is used; the problem is to choose, based on observations, the best estimator within this family. The paper studies a method for choosing a projection estimator, based on the principle of penalized empirical risk minimization. For this estimation method, nonasymptotic inequalities controlling its quadratic risk are given.
This publication is cited in the following 2 articles:
G. K. Golubev, “Exponential weighting and oracle inequalities for projection estimates”, Problems Inform. Transmission, 48:3 (2012), 271–282
A. Babenko, E. Belitser, “Oracle convergence rate of posterior under projection prior and Bayesian model selection”, Math. Meth. Stat., 19:3 (2010), 219