VARIABLE SELECTION AND ESTIMATION IN HIGH-DIMENSIONAL VARYING-COEFFICIENT MODELS.

Nonparametric varying coefficient models are useful for studying the time-dependent effects of variables. Many procedures have been developed for estimation and variable selection in such models. However, existing work has focused on the case when the number of variables is fixed or smaller than the sample size. In this paper, we consider the problem of variable selection and estimation in varying coefficient models in sparse, high-dimensional settings when the number of variables can be larger than the sample size. We apply the group Lasso and basis function expansion to simultaneously select the important variables and estimate the nonzero varying coefficient functions. Under appropriate conditions, we show that the group Lasso selects a model of the right order of dimensionality, selects all variables with the norms of the corresponding coefficient functions greater than certain threshold level, and is estimation consistent. However, the group Lasso is in general not selection consistent and tends to select variables that are not important in the model. In order to improve the selection results, we apply the adaptive group Lasso. We show that, under suitable conditions, the adaptive group Lasso has the oracle selection property in the sense that it correctly selects important variables with probability converging to one. In contrast, the group Lasso does not possess such oracle property. Both approaches are evaluated using simulation and demonstrated on a data example.