Model selection for varying coefficient nonparametric transformation model
基于平滑偏秩损失函数，提出了群LASSO惩罚估计量，用于变系数非参数变换模型，能同时选择重要变量并区分变系数与常系数，通过波士顿房价数据展示了其优势。
Summary Based on the smoothed partial rank (SPR) loss function, we propose a group LASSO penalized SPR estimator for the varying coefficient nonparametric transformation models, and derive its estimation and model selection consistencies. It not only selects important variables, but is also able to select between varying and constant coefficients. To deal with the computational challenges in the rank loss function, we develop a group forward and backward stagewise algorithm and establish its convergence property. An empirical application of a Boston housing dataset demonstrates the benefit of the proposed estimators. It allows us to capture the heterogeneous marginal effects of high-dimensional covariates and reduce model misspecification simultaneously that otherwise cannot be accomplished by existing approaches.