Optimal testing using combined test statistics across independent studies
Authors: Lasse Vuursteen, Botond Szabo, Aad van der Vaart, Harry van Zanten
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we investigate the numerical performance of the testing strategies outlined in Section 2.1 on synthetic data sets. We compare the tests based on their receiver operating characteristic (ROC) curve. |
| Researcher Affiliation | Academia | Lasse Vuursteen Delft Institute of Applied Mathematics Delft University of Technology l.vuursteen@tudelft.nl Botond Szabó Department of Decision Sciences and Institute for Data Science and Analytics Bocconi University botond.szabo@unibocconi.it Aad van der Vaart Delft Institute of Applied Mathematics Delft University of Technology a.w.vandervaart@tudelft.nl Harry van Zanten Mathematics Department Vrije Universiteit Amsterdam j.h.van.zanten@vu.nl |
| Pseudocode | No | The paper describes methods mathematically and textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository links or explicit statements of code release) for the source code of its described methodology. |
| Open Datasets | No | The paper states 'In our simulations we set m = 20, n = 30, let d range from 2 to 20 and take 2 = d/(4n)... For each level 2 {0.01, 0.02, . . . , 0.99} we compute the power for different combination strategies 100 times, each time drawing a different f 2 Rd with kfk2 = according to fi = d 1/2 Ri and Ri iid Rademacher random variables for i = 1, . . . , d.' This describes how synthetic data was generated, but no publicly available dataset is mentioned or linked. |
| Dataset Splits | No | The paper describes generating synthetic data for simulations and computing ROC curves, but it does not specify explicit train/validation/test dataset splits or cross-validation methodologies. |
| Hardware Specification | No | The paper describes the simulation setup and parameters in Section 4 but does not specify any hardware details (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or tools with version numbers required to reproduce the experiments. |
| Experiment Setup | Yes | In our simulations we set m = 20, n = 30, let d range from 2 to 20 and take 2 = d/(4n). ... For each level 2 {0.01, 0.02, . . . , 0.99} we compute the power for different combination strategies 100 times, each time drawing a different f 2 Rd with kfk2 = according to fi = d 1/2 Ri and Ri iid Rademacher random variables for i = 1, . . . , d. |