For high-dimensional data, particularly when the number of predictors greatly exceeds the sample size, selection of relevant predictors for regression is a challenging problem. Methods such as sure screening, forward selection, or penalized regressions are commonly used. Bayesian variable selection methods place prior distributions on the parameters along with a prior over model space, or equiv...

This paper introduces an orthogonal forward regression (OFR) model structure selection algorithm based on the Mestimators. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified GramSchmidt procedure. In this manner the OFR algorithm is extended to bad data conditions with improved performance due to M-estimators’ inherent robustness to outliers. An illus...

We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality. The optimal subset of features was calculated by means of a backward selection procedure. The performances of a probabilistic classifier modeled by means of a Kernel Probability Density Estimation method (KPDE) were compared with those of a feed-for...