Robust sampling designs for a possibly misspecified stochastic process
Why this work is in the frame
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Bibliographic record
Abstract
We address the problem of finding robust sampling designs for the estimation of a discrete time second-order stationary process when its autocorrelation function is only approximately specified and has a spectral density belonging to a neighbourhood of a specified ‘base’ density. The value of the stochastic process is predicted by the best – for the assumed autocorrelation function – linear unbiased predictor on the basis of a finite sample of observations. Following the approach of minimax robustness, we find the least favourable – in the sense of maximizing the average mean squared error (amspe) of these predictions – spectral density. We then obtain, through a genetic algorithm, robust sampling designs which minimize this maximum amspe. Several examples are discussed and assessed, on the basis of which we conclude that the robust designs can offer substantial protection against model errors, at a minimal cost in efficiency at the base model. The techniques are illustrated in a case study, using a series of interest in statistical climatology.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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