Selection and combination of biomarkers using ROC method for disease classification and prediction

Based on the SCAD penalty and the area under the ROC curve (AUC), we propose a new method for selecting and combining biomarkers for disease classification and prediction. The proposed estimator for the combination of the biomarkers has an oracle property; that is, the estimated combination of the biomarkers performs as well as it would have been if the biomarkers significantly associated with the outcome had been known in advance, in terms of discriminative power. The proposed estimator is computationally feasible, n^1/2‐consistent and asymptotically normal. Simulation studies show that the proposed method performs better than existing methods. We illustrate the proposed methodology in the acoustic startle response study. The Canadian Journal of Statistics 39: 324–343; 2011 © 2011 Statistical Society of Canada     

Variable selection and estimation for multivariate panel count data via the seamless‐${\it L}_{{\rm 0}}$ penalty

This paper considers regression analysis of multivariate panel count data with the focus on variable selection and estimation of significant covariate effects. For the problem, we adopt the penalized estimating equation approach with a focus on the use of the seamless‐$L_0$ penalty. The proposed approach selects variables and estimates regression coefficients simultaneously and the asymptotic properties of the resulting estimates are established. The procedure can be easily carried out with the Newton–Raphson algorithm and is evaluated by simulation studies. Also it is applied to a motivating data set arising from a skin cancer study. The Canadian Journal of Statistics 41: 368–385; 2013 © 2013 Statistical Society of Canada