This paper studies shrinkage estimation of a general varying-coefficient model in Li and Racine (2010), with both continuous and categorical covariates. We propose a kernel least absolute shrinkage and selection operator (KLASSO) to implement estimation and variable selection for the model. We establish the estimation sparsity and oracle efficiency of the KLASSO estimator. We also provide a BIC-type criterion for tuning parameter selection and justify the model selection consistency. Simulation results suggest our method has a nice performance in terms of estimation errors and variable selection.

中文翻译：

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具有连续和分类协变量的变系数模型的收缩率估计

本文研究了Li和Racine（2010）中具有连续协变量和分类协变量的一般变系数模型的收缩估计。我们提出了一个内核最小绝对收缩和选择算子（KLASSO）来实现模型的估计和变量选择。我们建立了KLASSO估计器的估计稀疏度和预言效率。我们还提供用于调整参数选择的BIC类型准则，并证明模型选择的一致性。仿真结果表明我们的方法在估计误差和变量选择方面具有良好的性能。