Feature screening of ultrahigh dimensional longitudinal data based on the C‐statistic

AbstractThis paper considers the feature screening method for the ultrahigh dimensional semiparametric linear models with longitudinal data. The C‐statistic which measures the rank concordance between predictors and outcomes is generalized to the longitudinal data. On the basis of C‐statistic and the score equation theory, we propose a feature screening method named LCSIS. Based on the smoothed technique and the score equations, the proposed estimating screening procedure is easy to compute and satisfies the feature screening consistency. Furthermore, Monte Carlo simulation studies and a real data application are conducted to examine the finite sample performance of the proposed procedure.