We propose for a quantile regression an estimation method for transferring knowledge using two $L_1$ penalties based on an estimator obtained from a source database. The proposed transfer learning estimator satisfies the properties of consistency and sparsity. Its convergence rate and asymptotic behavior are studied in several scenarios. This knowledge transfer results in a shorter computation time than that of the standard adaptive LASSO estimator. Another advantage of our method is that it can be applied to models with non-Gaussian errors. In addition, in order to implement the computing of the adaptive transfer LASSO quantile estimator, we propose an algorithm. The simulations confirm the theoretical results and demonstrate that the adaptive learning estimator, calculated using the proposed algorithm, is more competitive than the LASSO estimators. Finally, we illustrate the practical utility of the proposed transfer learning estimator and algorithm using a real-data application involving the physicochemical properties of protein tertiary structures.
🌊
