The Function-on-Scalar LASSO with Applications to Longitudinal GWAS

摘要: We present a new methodology for simultaneous variable selection and parameter estimation in function-on-scalar regression with an ultra-high dimensional predictor vector. extend the LASSO to functional data both $\textit{dense}$ setting $\textit{sparse}$ setting. provide theoretical guarantees which allow exponential number of variables. Simulations are carried out illustrate compare sparse/functional methods. Using Framingham Heart Study, we demonstrate how our tools can be used genome-wide association studies, finding genetic mutations affect blood pressure therefore important cardiovascular health.