Reproducing Kernel Hilbert space for penalized regression multi-predictors: Case in longitudinal data

Penalized regression procedures have become a very popular approach to estimating the curve of nonparametric regression in longitudinal data. Reproducing Kernel Hilbert Space (RKHS) is Reproducing Kernel Hilbert Space (RKHS) play a central role in Penalized Regression as a form and estimator function of the model. The aim of this study are to solve the estimation of Penalized Regression using RKHS, and apply the Penalized Regresion using secondary dataset. The Penalized Regresssion using RKHS is. The estimation of with: The aplication of data results show that the spline estimator can be applied to the generation of data with m = 4 (cubic spline) which gives the value of R²of 97.77%.