A Regularization-Based Adaptive Test for High-Dimensional GLMs
Authors: Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan
JMLR 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Via simulations, its superior finite-sample performance is demonstrated over several representative existing methods. In addition, we apply it and other representative tests to an Alzheimer s Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer s disease. Sections 3 and 4 are titled "Simulations" and "Real data analyses" respectively. |
| Researcher Affiliation | Academia | Chong Wu EMAIL Department of Statistics, Florida State University, FL, USA; Gongjun Xu EMAIL Department of Statistics, University of Michigan, MI, USA; Xiaotong Shen EMAIL School of Statistics, University of Minnesota, MN, USA; Wei Pan EMAIL Division of Biostatistics, University of Minnesota, MN, USA. |
| Pseudocode | No | The paper describes methods mathematically and in paragraph text but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have released open source R package aispu implementing the proposed test on Git Hub (https://github.com/Chong Wu-Biostat/aispu), and will upload it to CRAN soon. |
| Open Datasets | Yes | In addition, we apply it and other representative tests to an Alzheimer s Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer s disease. See www.adni-info.org for the latest information. |
| Dataset Splits | No | The paper mentions generating 1,000 datasets for simulation and sampling n/2 cases and n/2 controls for logistic regression simulation. For real data, it describes subject grouping for outcome definition but does not specify standard training/test/validation splits needed to reproduce experiments. |
| Hardware Specification | No | The acknowledgments mention the "Minnesota Supercomputing Institute" as a resource, but no specific hardware components like GPU or CPU models, or memory details are provided. |
| Software Dependencies | No | The paper mentions using "pmvnorm() in R package mvrnorm" and that they have released "R package aispu". However, it does not provide specific version numbers for these R packages or for R itself, which is required for reproducibility. |
| Experiment Setup | Yes | For each simulation setting, we generated 1,000 data sets to evaluate the empirical size and power at the significance level α = 0.05. The candidate set of γ for the ai SPU was taken to be Γ = {1, 2, . . . , 6, } unless otherwise stated. We set ρ = 0 to generate independent SNPs unless otherwise stated. We set ϑ1 = ϑ2 = 0.4 and other ϑj = 0. We set n = 200, q = 1000, and p = 1000. To account for multiple testing, we applied the Bonferroni correction and used a slightly conservative cutoff0.05/100 = 5 10 4. |