Information theory and mixing least-squares regressions
For Gaussian regression, we develop and analyze methods for combining estimators from various models. For squared-error loss, an unbiased estimator of the risk of the mixture of general estimators is developed. Special attention is given to the case that the component estimators are least-squares projections into arbitrary linear subspaces, such as those spanned by subsets of explanatory variables in a given design. We relate the unbiased estimate of the risk of the mixture estimator to estimates of the risks achieved by the components. This results in simple and accurate bounds on the risk and its estimate, in the form of sharp and exact oracle inequalities. That is, without advance knowledge of which model is best, the resulting performance is comparable to or perhaps even superior to what is achieved by the best of the individual models. Furthermore, in the case that the unknown parameter has a sparse representation, our mixture estimator adapts to the underlying sparsity. Simulations show that the performance of these mixture estimators is better than that of a related model-selection estimator which picks a model with the highest weight. Also, the connection between our mixtures with Bayes procedures is discussed.
oracle inequalities sparsity bayes mixtures combining least-squares regressions complexity model adaptation model selection target resolvability unbiased risk estimate