Asymptotic properties of adaptive Lasso penalized least squared method of generalized linear models with adaptive designs
GAO Qibing*, YU Huan, SHI Qianqian, ZHU Guimei
(School of Mathematical Sciences, Nanjing Normal University, Nanjing 210046, Jiangsu, China)
Abstract:
For generalized linear models with adaptive designs, the variable selection method based on the adaptive Lasso penalized least squared method is considered. Under certain conditions, the consistency and oracle properties of the adaptive Lasso penalized least squared estimator are established, which extend the corresponding results from the fixed designs to the adaptive designs for generalized linear models. The simulation results show that the adaptive Lasso penalty method is better than Lasso punishment.
KeyWords:
generalized linear models;adaptive Lasso;variable selection;Oracle property
