Accuracy of the estimator of Gaussian autoregressive process
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Bibliographic record
Abstract
The accuracy of the estimator of the Gaussian AR process is studied depending on pole locations. Three types of AR processes, i.e., broadband AR, narrowband AR, and mixed-band AR, are defined and their theoretical limits of estimation accuracy are assessed in terms of the exact Cramer-Rao bound (CRB). The accuracy decreases as the pole closest to the origin gets closer to the origin and each coefficient parameter can show fairly different accuracy especially in the narrow-band case. The AR parameters are also estimated by applying two well-known estimation methods - the autocorrelation method and Burg's method. A typical way of reducing the estimation variance is the averaging of multiple test-runs. But it turns out that a long data record is more important than the number of test-runs to obtain a highly accurate estimation in the narrowband case, and vice versa in the broadband case.
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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 | 0.000 | 0.000 |
| 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 it