Notice: 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. Full accuracy metrics and methodology are described in [1].
Multivariate tests of association based on univariate tests
Authors: Ruth Heller, Yair Heller
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 we demonstrate in simulations that novel tests based on our approach can have both a power advantage and a great computational advantage over existing multivariate tests. In order to assess the effect of using our novel approach, we carry out experiments. We have three specific aims: (1) to compare the power of using a single center point versus multiple center points; (2) to assess the effect of different univariate tests on the power; and (3) to see how the resulting tests fare against other multivariate tests. |
| Researcher Affiliation | Academia | Ruth Heller Department of Statistics and Operations Research Tel-Aviv University Tel-Aviv, Israel 6997801 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper does not provide concrete access information for a publicly available or open dataset. It defines distributions (e.g., F1 = N2{(0, 0), diag(1, 1)}) from which data is simulated rather than using pre-existing datasets with access details. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning for training, validation, or testing. It states: "The sample size in each group was 100" but no splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings, as the experiments are statistical simulations rather than machine learning model training. It mentions "The sample size in each group was 100" and |