Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality

Variable selection methods have been a focus in the context of econometrics and statistics literature. In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse boosting algorithm to conduct model-based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time-consuming selection of tuning parameters. Extensive numerical examples illustrate the advantage of the proposed methodology. An application of Boston housing price data is further provided to demonstrate the proposed methodology.