False discovery proportion envelopes with m-consistency
Authors: Meah Iqraa, Blanchard Gilles, Roquain Etienne
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These improvements are illustrated with numerical experiments and real data examples. In particular, the improvement is significant in the knockoffs setting, which shows the impact of the method for a practical use. |
| Researcher Affiliation | Academia | Iqraa Meah EMAIL Center for Research in Epidemiology and Statistic S (CRESS) Universit e Paris Cit e and Universit e Sorbonne Paris Nord, Inserm, INRAE F-75004 Paris, France; Gilles Blanchard EMAIL Institut Math ematiques de Orsay (IMO) CNRS, Inria, Universit e Paris-Saclay F-91405 Orsay Cedex; Etienne Roquain EMAIL Laboratoire de Probabilit es, Statistique et Mod elisation, CNRS, Sorbonne Universit e, Universit e de Paris. Sorbonne Universit e 4 place Jussieu, 75005, Paris |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. Procedures are described mathematically or in narrative text. |
| Open Source Code | Yes | All our numerical experiments are reproducible from the code provided in the repository https://github.com/iqm15/Consistent FDP. |
| Open Datasets | Yes | We now consider the online case, by applying our method to the real data example coming from the International Mice Phenotyping Consortium (IMPC) (Mu noz-Fuentes et al., 2018) |
| Dataset Splits | No | The paper describes simulation setups and theoretical models (e.g., Gaussian location model, VCT model) for generating data for numerical experiments, but does not specify training/test/validation splits of any publicly available datasets in the machine learning sense. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) used for the experiments. |
| Experiment Setup | Yes | Throughout the experiments, the default value for δ is 0.25 and the default number of replications to evaluate each FDP bound is 1000. To investigate the consistency property, we take m varying in the range {10i, 2 i 6}, and we consider the FDP bounds FDP Simes α (16), FDP DKW α (17), FDP KR α (18), FDP Well α (19) for α {0.05, 0.1, 0.15, 0.2}. |