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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".