l1-norm quantile regression screening rule via the dual circumscribed sphere

超高维下可加分位数回归的非参数筛选

Nonparametric Screening for Additive Quantile Regression in Ultra-high Dimension

针对超高维非参数可加分位数回归中边缘筛选易漏选或误选协变量的问题，提出一种条件非参数分位数筛选程序，能以高概率准确识别所有相关协变量。

StaRQR-K：受控于虚假发现率的区域分位数回归

StaRQR-K: False Discovery Rate Controlled Regional Quantile Regression

StaRQR-K通过控制虚假发现率实现区域分位数回归，用于检测基因组特征与结果分布特定分位数区域的关联。

高维稀疏组卷积平滑分位数回归

Sparse-group Convoluted Smoothed Quantile Regression in High Dimension

针对高维场景下传统组选择方法无法实现组内稀疏的问题，提出自适应两阶段稀疏组选择方法，结合卷积平滑分位数损失与两类惩罚项实现组内双水平稀疏，兼具理论保障与计算效率，在模拟及乳腺癌基因数据中表现优异。

Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression

We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings. Unlike existing coordinate descent type algorithms, the SNCD updates each regression coefficient and its corresponding subgradient simultaneously in each iteration. It combines the strengths of the coordinate descent and the semismooth Newton algorithm, and effectively solves the computational challenges posed by dimensionality and nonsmoothness. We establish the convergence properties of the algorithm. In addition, we p…

Nonparametric Screening under Conditional Strictly Convex Loss for Ultrahigh Dimensional Sparse Data

Sure screening technique has been considered as a powerful tool to handle the ultrahigh dimensional variable selection problems, where the dimensionality p and the sample size n can satisfy the NP dimensionality log p=O(n^a) for some a>0 (Fan & Lv 2008). The current paper aims to simultaneously tackle the "universality" and "effectiveness" of sure screening procedures. For the "universality", we develop a general and unified framework for nonparametric screening methods from a loss function perspective. Consider a loss function to measure the divergence of the response variable and the underly…

具有大量输入的计算机实验中不确定性传播的先进方法

Advanced methodology for uncertainty propagation in computer experiments with large number of inputs

本文提出了一种结合空间填充设计、筛选和高斯过程的先进方法，用于处理大量输入的计算机实验中的不确定性传播和敏感性分析。