Feature screening for time-varying coefficient models with ultrahigh-dimensional longitudinal data

Abstract

Motivated by an empirical analysis of the Childhood Asthma Management Project, CAMP, we introduce a new screening procedure for varying coefficient models with ultrahigh-dimensional longitudinal predictor variables. The performance of the proposed procedure is investigated via Monte Carlo simulation. Numerical comparisons indicate that it outperforms existing ones substantially, resulting in significant improvements in explained variability and prediction error. Applying these methods to CAMP, we are able to find a number of potentially important genetic mutations related to lung function, several of which exhibit interesting nonlinear patterns around puberty.