Signal and System Minimum Mismatch Modeling (3M) with Order Selection Analysis
Bibliographic record
Abstract
In the data-driven modeling of linear, time-invariant structures, the choice of the number of parameters (order selection) is critical. It is known that the modeling error, resulting from the maximum likelihood estimate, cannot be directly used for order estimation as this error is a monotonically decreasing function of model order. The most well-known order selection methods, including Akaike information criterion (AIC), Bayesian information criterion (BIC), and minimum description length (MDL), propose an additive penalty term to the modeling error. A more recently proposed method, reconstruction error minimization (REM) concentrates on a different error to provide optimal order selection using a statistical learning approach. A closed-form expression of REM has been provided for the order selection in linear models, including finite impulse responses and has shown superiority over other order selection methods. The existing method of REM calculation uses a Gaussian approximation of the Chi- squared distribution. This work first provides the exact modeling of REM using the Chi-squared distribution. Next, the use of REM is extended for all-pole modeling and pole-zero modeling. For autoregressive (AR) order selection, a method denoted by minimum mismatch modeling (3M) is introduced. It has also been shown that REM is a special case of 3M. Simulation results show the advantages of the proposed method over the existing order selection methods by avoiding overparametrization or underpamaterization in favour of mean squared error (MSE) minimization. In addition, a practical application of eye blink artifact removal from electroencephalogram (EEG) data shows that the proposed method efficiently models the true background EEG while eliminating artifacts efficiently. Furthermore, the application of 3M for EEG sleep-stage classification enabled the classification process to be automated. It is worth mentioning that the use of 3M shows that different sleep-stages have different orders, which can be further used as a feature for classification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".