High-Dimensional Statistical Inference and Variable Selection Using Sufficient Dimension Association
利用充分维度关联进行高维统计推断与变量选择
Simultaneous variable selection and statistical inference is challenging in high-dimensional data analysis. Most existing post-selection inference methods require explicitly specified regression models, which are often linear, as well as sparsity in the regression model. The performance of such procedures can be poor under either misspecified nonlinear models or a violation of the sparsity assumption. In this article, we propose a sufficient dimension association (SDA)…