Unified inference for longitudinal/functional data quantile dynamic additive models
Bibliographic record
Abstract
Abstract We investigate the unified inference of a time‐varying additive model under the quantile regression framework, considering both sparse and dense longitudinal or functional data. For convolution‐type smoothed objective functions, we propose a two‐step method for estimating both the trend and the component functions. Theoretical analysis shows that the two‐step estimators share the same asymptotic distribution as the oracle estimators, while the convergence rates and limiting variance functions differ between sparse and dense situations. However, making a subjective choice between these two cases can lead to incorrect statistical inferences. To address this issue, we develop sandwich formulas for variance estimations. This allows us to establish a unified inference without the need to decide whether the data are sparse or dense. Via simulation studies, we assess the finite‐sample performance of the proposed methods. Finally, analyses of two different types of real data illustrate our proposed methods.
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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 | 0.001 | 0.014 |
| 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) | 0.000 | 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".