Optimal subsampling for large‐sample quantile regression with massive data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To balance the explosive growth of data volume and limited budgets for computational resources, one of the popular methods is downscaling the data volume by subsampling a subdataset that inherits the relevant property of the full data. As an alternative to the mean regression model, the quantile regression model has been studied extensively when the data are independent and the data scale is medium. This article focuses on quantile regression with massive data where the sample size n (greater than in general) is extraordinarily large but the dimension d (smaller than 20 in general) is small. We first formulate the general subsampling procedure and establish the asymptotic property of the resultant estimator. Then, with the help of optimality criteria in experimental design, we derive two subsampling probabilities that are optimal in the sense of smallest asymptotic mean square error. Since the optimal subsampling probabilities depend on the full data estimator, we develop a two‐step optimal subsampling algorithm and study the consistency and asymptotic normality of the resultant estimator. The empirical performance of the optimal subsampling algorithm is evaluated with synthetic and real datasets.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it