Linearity Identification for General Partial Linear Single-Index Models

Partial linear models, a family of popular semiparametric models, provide us with an interpretable and flexible assumption for modelling complex data. One challenging question in partial linear models is the structure identification for the linear components and the nonlinear components, especially for high dimensional data. This paper considers the structure identification problem in the general partial linear single-index models, where the link function is unknown. We propose two penalized methods based on a modern dimension reduction technique. Under certain regularity conditions, we show that the second estimator is able to identify the underlying true model structure correctly. The convergence rate of the new estimator is established as well.