In this paper, we consider simultaneous model selection and estimation for the additive index model. The additive index model is a class of structured nonparametric models that can be expressed as additive models of a set of unknown linear transformation of the original predictor variables. We introduce a penalized least squares estimator and discuss how it can be efficiently computed in practice. Both theoretical and empirical properties of the estimate are presented to demonstrate its merits. Extensions to more general prediction framework are also discussed
