Structure detection of nonlinear dynamic systems using bootstrap methods [and biomedical application]
Bibliographic record
Abstract
Identification of NARMAX models involves estimating unknown parameters and detecting its underlying structure, which entails selecting a set of parameters to give a parsimonious description of the system. In the present study a bootstrap based structure detection algorithm is investigated. The bootstrap method is a numerical procedure for estimating parameter statistics that requires few assumptions. Its use for structure detection maintains the simplicity of routines developed for linear regression estimators but requires a less restrictive set of assumptions. The performance of this bootstrap structure detection technique was evaluated by using it to estimate the structure of a simple NARMAX model and comparing the results to those with the t-test and stepwise regression. Applicability of the method to more complex systems such as ones encountered in biomedical applications, was shown by identifying a parsimonious system description of the ankle model. Moreover, we showed that the bootstrap method yields parameter statistics that are closer to optimal than using traditional methods. The proposed method is simple to use and is robust in the presence of noise.
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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 | 0.014 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".