Exact Post Model Selection Inference for Marginal Screening
Authors: Jason Lee, Jonathan E Taylor
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 7, we evaluate the methodology on two real datasets. In Figure 1, we have already seen that the confidence intervals constructed using Algorithm 2 have exactly 1 α coverage proportion. In this section, we perform two experiments on real data where the linear model does not hold, the noise is not Gaussian, and the noise variance is unknown. |
| Researcher Affiliation | Academia | Jason D. Lee Computational and Mathematical Engineering Stanford University Stanford, CA 94305 jdl17@stanford.edu Jonathan E. Taylor Department of Statistics Stanford University Stanford, CA 94305 jonathan.taylor@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Marginal screening algorithm |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The diabetes dataset contains n = 442 diabetes patients measured on p = 10 baseline variables [6]. |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits with percentages, sample counts, or references to predefined standard splits for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or cloud computing resources) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | The confidence intervals were constructed for the k = 2 variables selected by the marginal screening algorithm. The z-test intervals were constructed via (4) with α = .1... |