Robust estimation of LP parameters in white noise with unknown variance
Bibliographic record
Abstract
The problem of robust estimation of the linear prediction (LP) parameters for an autoregressive process (AR) in white noise is addressed in this paper. The classical solution to this problem involves using the p low-order Yule-Walker equations and subtracting an estimate of the noise variance from the main diagonal of the correlation matrix. However, this approach lacks robustness against possible oversubtraction of the noise variance. In such a case, the resulting estimate of the correlation matrix won't constrain to be positive-definite. The main contribution of this paper is the combination of an appropriate noise variance estimator with an effective processing scheme to circumvent the problem mentioned above. The noise variance, which determines the bias in the standard least-squares criterion, is estimated using the overdetermined normal equations, the truncated singular value decomposition and the correlation matching property. It is shown that for an AR process in additive white noise, the present method performs better than that proposed by the authors in previous related work.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".