Modelling High-Dimensional Categorical Data using Nonconvex Fusion Penalties
利用非凸融合惩罚建模高维分类数据
Abstract We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding coefficients exactly equal. This is achieved using the minimax concave penalty on differences between the order statistics of the coefficients for a categorical variable, thereby clustering the coefficients. We provide an algorithm for exact and efficient computation of the global minimum…