On the consistency theory of high dimensional variable screening
Authors: Xiangyu Wang, Chenlei Leng, David B. Dunson
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This article studies a class of linear screening methods and establishes consistency theory for this special class. In particular, we prove the restricted diagonally dominant (RDD) condition is a necessary and sufficient condition for strong screening consistency. As concrete examples, we show two screening methods SIS and HOLP are both strong screening consistent (subject to additional constraints) with large probability if n > O((ρs+σ/τ)2 log p) under random designs. |
| Researcher Affiliation | Academia | Xiangyu Wang Dept. of Statistical Science Duke University, USA xw56@stat.duke.edu; Chenlei Leng Dept. of Statistics University of Warwick, UK C.Leng@warwick.ac.uk; David B. Dunson Dept. of Statistical Science Duke University, USA dunson@stat.duke.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on consistency theory under random designs (e.g., Gaussian distribution for X and ϵ). It does not use specific publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not present empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |