Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Multiple Testing under Dependence via Semiparametric Graphical Models
Authors: Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 18,115 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,583 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A variety of simulations show that our semiparametric approach outperforms classical procedures which assume independence and the parametric approaches which capture dependence. |
| Researcher Affiliation | Academia | Jie Liu EMAIL Department of Computer Sciences, University of Wisconsin-Madison Chunming Zhang EMAIL Department of Statistics, University of Wisconsin-Madison Elizabeth Burnside EMAIL Department of Radiology, University of Wisconsin-Madison David Page EMAIL Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison |
| Pseudocode | No | The paper describes the algorithmic steps and modifications in narrative text, but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We apply our procedure to a real-world GWAS on breast cancer (Hunter et al., 2007) which involves 528,173 SNPs for 1,145 cases and 1,142 controls. |
| Dataset Splits | No | The paper mentions using simulated data for experiments and 'a second cohort to validate the 18 SNPs' in the real-world application, but it does not specify explicit training/validation/test dataset splits with percentages or sample counts for a single dataset. |
| Hardware Specification | Yes | In the chain-structure simulations, it took our data-driven procedure about 10 hours to finish the 500 replications sequentially (for one µ value in (10)) on one 3GHz CPU. In the grid-structure simulations, it took our procedure around 30 hours to finish the 500 replications sequentially (for one µ value in (10)) on one 3GHz CPU. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We consider two dependence structures, namely a chain structure and a grid structure. For the chain structure, we choose the number of hypotheses m=10,000. For the grid structure, we choose a 100 × 100 grid, which also yields 10,000 hypotheses. We test two levels of dependence strength, i.e. φ=0.8 and φ=0.6. We set π to be 0.4. We set λ=0.8, and the value of p0 is estimated to be 0.978. |