Automatic Order Selection in Autoregressive Modeling with Application in EEG Sleep-Stage Classification
Bibliographic record
Abstract
This paper investigates the order selection problem for autoregressive models from a new perspective. It is known that the modeling error is a decreasing function of the model complexity and cannot directly be used for order selection. In the proposed approach, denoted by minimum mismatch modeling error (3ME), the modeling error is used to estimate the 3ME which is the true representation of the optimum order. The proposed approach provides probabilistic upper-bounds on the mismatch modeling error using a statistical learning approach. Simulation results on generated synthetic data shows advantages of the 3ME method compared to existing order selection methods such as AIC and BIC as it avoids model overparametrizing or underparametrizing and improves the accuracy. 3ME can automate AR order selection which is a valuable feature. As shown in the simulation results for sleep-stage classification, the automated estimated order can be used as an additional feature in the classification process to increase accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".