Adaptive Estimation for High-Dimensional Quantile Regression with Misspecification and Nonresponse
Bibliographic record
Abstract
In high-dimensional data analysis, most sure independence screening (SIS) procedures are significantly affected by both misspecification and missing data, making the results sensitive to the loss of predictive accuracy.On the other hand, classical model averaging methods are typically limited to well-specified structures or imposed restrictive constraints on candidates.To address the gaps, this paper focuses on the conditional quantile estimation in conjunction with inverse probability weighting, the purposes of which are mainly threefold.Firstly, we study the SIS properties under misspecified quantile models.Secondly, we propose an adaptive model averaging algorithm for complex clusters.Thirdly, we develop a robust improvement strategy to enhance asymptotic efficiency with respect to high-dimensional ignorable mechanism.Theoretical properties of the averaging estimator are investigated, including its finite sample performance, the equivalence between adaptation and asymptotic optimality, as well as the consistency of weights.Numerical simulations illustrate the method's ability to efficiently identify the correct specification and maintain resilience against outliers in response probabilities.The real-data example is analyzed to validate our method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".