Structure detection of NARMAX models using bootstrap methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systems described by nonlinear difference equations, linear-in-the-parameters, which expand the current output in terms of present and past inputs and past outputs are considered NARMAX (Nonlinear AutoRegressive, Moving Average eXogenous) models. Many systems are described by NARMAX models using only a few terms. However, depending on the order of the system, the number of candidate terms can be very large. Selection of a subset of these candidate terms is necessary for an efficient system description. This remains an unresolved issue in system identification for over-parameterized models. A bootstrap based structure detection algorithm is proposed as a means of determining the structure of highly over-parameterized models. The performance of our bootstrap structure detection technique was evaluated by using it to estimate the structure of two NARMAX models, with colored input and white, zero-mean, output additive noise, and comparing the results to those of the t-test and stepwise regression. The proposed method is simple to use and is robust in the presence of noise.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it