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高维广义线性模型部分惩罚检验的强 Oracle 保证AI 解读

Abstract Partial penalized tests provide flexible approaches to testing linear hypotheses in high-dimensional generalized linear models. However, because the estimators used in these tests are local minimizers of potentially nonconvex folded-concave penalized objectives, the solutions one computes in practice may not coincide with the unknown local minima for which we have nice theoretical guarantees. To close this gap between theory and computation, we introduce local…

基于惩罚 Tobit 似然的高维删失回归AI 解读

High-dimensional regression and regression with a left-censored response are each well-studied topics. In spite of this, few methods have been proposed which deal with both of these complications simultaneously. The Tobit model—long the standard method for censored regression in economics—has not been adapted for high-dimensional regression at all. To fill this gap and bring up-to-date techniques from high-dimensional statistics to the field of high-dimensional left-censored…