Regression models are introduced into the receiver operating characteristic (ROC) analysis to accommodate effects of covariates, such as genes. If many covariates are available, the variable selection issue arises. The traditional induced methodology separately models outcomes of diseased and nondiseased groups; thus, separate application of variable selections to two models will bring barriers...

The Bayesian variable selection method proposed in the paper is based on the evaluation of the Kullback-Leibler distance between the full (or encompassing) model and the submodels. The implementation of the method does not require a separate prior modeling on the submodels since the corresponding parameters for the submodels are deened as the Kullback-Leibler projections of the full model param...

In order to develop regression/classification models, QSAR analysis typically uses molecular descriptors as independent variables. The number of molecular descriptors has hugely increased over time and nowadays thousands of descriptors, able to describe different aspects of a molecule, can be calculated by means of dedicated software. However, when modelling a particular property or biological ...

Variable selection is fundamental to high-dimensional statistical modeling. Many variable selection techniques may be implemented by maximum penalized likelihood using various penalty functions. Optimizing the penalized likelihood function is often challenging because it may be nondifferentiable and/or nonconcave. This article proposes a new class of algorithms for finding a maximizer of the pe...

One of the main topic in the development of predictive models is the identification of variables which are predictors of a given outcome. Automated model selection methods, such as backward or forward stepwise regression, are classical solutions to this problem, but are generally based on strong assumptions about the functional form of the model or the distribution of residuals. In this paper a...

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This paper describes and compares various hierarchical mixture prior formulations of variable selection uncertainty in normal linear regression models. These include the nonconjugate SSVS formulation of George and McCulloch (1993), as well as conjugate formulations which allow for analytical simplification. Hyperparameter settings which base selection on practical significance, and the implicat...

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Partial correlation based variable selection method was proposed for normal linear regression models by Bühlmann, Kalisch and Maathuis (2010) as a comparable alternative method to regularization methods for variable selection. This paper addresses two important issues related to partial correlation based variable selection method: (a) whether this method is sensitive to normality assumption, an...