Bibliographic record
Abstract
ABSTRACT Ullah and Wang ( Journal of Time Series Analysis , 46 (4), 748–773) introduced a novel nonparametric estimator for the volatility function that leverages the mode to complement traditional mean‐based volatility measures, thereby highlighting unique features of the data. In this paper, we extend their framework to estimate traditional mean volatility by proposing a new approach, termed robust mode‐oriented volatility . Our method treats the bandwidth parameter associate with the kernel objective function as a constant rather than a shrinkage parameter, prioritizing robustness and efficiency while maintaining its foundation in the mode‐based framework. We demonstrate that under ‐mixing time series dependence, the proposed robust estimator retains the same asymptotic distribution as estimators derived under independence assumptions, while achieving the convergence rate of nonparametric mean regression. Furthermore, we theoretically establish that efficiency gains over traditional mean‐based estimation can be realized by appropriately adjusting the bandwidth, particularly in the presence of outliers or heavy‐tailed distributions. In contrast to Ullah and Wang ( Journal of Time Series Analysis , 46 (4), 748–773), which focused on capturing volatility structures specific to the mode, our approach broadens the applicability of their framework by integrating it with traditional mean volatility estimation. The discussions on bandwidth selection and numerical examples highlight the finite sample performance and practical advantages of our robust estimation procedure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".