Asymmetric influence measure for high dimensional regression
Bibliographic record
Abstract
Identification of influential observations is crucial in data analysis, particularly with high dimensional datasets, where the number of predictors is higher than the sample size. These rich datasets with extensive detail are increasingly exploited and analyzed in multiple fields of science, e.g., genomics, neuroscience, finance, etc. Unfortunately, classical diagnostic statistical tools are not tailored for identifying influential observations in high dimensional setup. In this paper, we use the concept of expectiles to develop an influence measure in high dimensional regression. The influence measure is based on the asymmetric marginal correlation, and its derived asymptotic distribution is used to define a threshold based on statistical principles. Our comprehensive simulation results display the favorable qualities of this influence measure under various scenarios. The usefulness of the proposed measure is illustrated through the analysis of a neuroimaging dataset. An R package implementing the procedure is publicly available on GitHub (https://github.com/AmBarry/hidetify).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".