Multiple Testing under Dependence via Semiparametric Graphical Models

Authors: Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page

ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

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 JIELIU@CS.WISC.EDU Department of Computer Sciences, University of Wisconsin-Madison Chunming Zhang CMZHANG@STAT.WISC.EDU Department of Statistics, University of Wisconsin-Madison Elizabeth Burnside EBURNSIDE@UWHEALTH.ORG Department of Radiology, University of Wisconsin-Madison David Page PAGE@BIOSTAT.WISC.EDU 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.