Simultaneous Inference for Massive Data: Distributed Bootstrap
Authors: Yang Yu, Shih-Kang Chao, Guang Cheng
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations validate our theory.Section 4 presents simulation results that corroborate our theoretical findings. |
| Researcher Affiliation | Academia | 1Department of Statistics, Purdue University, USA 2Department of Statistics, University of Missouri, USA. |
| Pseudocode | Yes | Algorithm 1 Dist Boots(method, e , {gj}k j=1, e ) |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available or provide a link to a code repository. |
| Open Datasets | No | For linear model, we generate e independently from N(0, 1), simulate the response from y = x> + e; for GLM, we consider logistic regression and obtain each response from y Ber(1/(1 + exp[ x> ])). This indicates the data was simulated, not from a public dataset. |
| Dataset Splits | No | The paper describes generating synthetic data for simulations and drawing bootstrap samples, but does not provide specific train/validation/test dataset splits in terms of percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Fix the total sample size N = 2^16. Choose d from {2^1, 2^3, 2^5, 2^7} and k from {2^0, 2^1, . . . , 2^11}. beta is determined by drawing uniformly from [ 0.5, 0.5]^d and keep it fixed for all replications. ...At each replication, we draw B = 500 bootstrap samples, from which we calculate the 95% empirical quantile to further obtain the 95% simultaneous confidence interval... |