Penalized estimation for non-identifiable models

Abstract: We derive asymptotic properties of penalized estimators for singular models for which identifiability may break and the true parameter values can lie on the boundary of the parameter space. Selection consistency of the estimators is also validated. The problem that the true values lie on the boundary is dealt with by our previous results that are applicable to singular models, besides, penalized estimation and non-ergodic statistics. In order to overcome non-identifiability, we consider a suitable penalty such as the non-convex Bridge and the adaptive Lasso that stabilizes the asymptotic behavior of the estimator and shrinks inactive parameters. Then the estimator converges to one of the most parsimonious values among all the true values. In particular, the oracle property can also be obtained even if parametric structure of the singular model is so complex that likelihood ratio tests for model selection are labor intensive to perform. Among many potential applications, the examples handled in the paper are: (i) the superposition of parametric proportional hazard models and (ii) a counting process having intensity with multicollinear covariates.