A bootstrap method for structure detection of NARMAX models
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Bibliographic record
Abstract
Abstract Many systems may be described by NARMAX models using only a few terms. However, depending on the order of the system the number of candidate terms can become very large. Selection of a subset of these candidate terms is necessary for an efficient system description. This is an unresolved issue in system identification for over-parameterized models. Therefore, in this paper, we develop a bootstrap structure detection (BSD) algorithm as a means of determining the structure of highly over-parameterized models. The performance of this BSD technique was evaluated by using it to estimate the structure of a (1) simple NARMAX model, (2) moderately over-parameterized NARMAX model and (3) highly over-parameterized NARMAX model. The results demonstrate that the BSD algorithm is a robust method for detecting the structure of NARMAX models. This method provides accurate estimates of parameter statistics without relying on assumptions made by traditional procedures and yields a parsimonious description of the system. Acknowledgments Supported by grants from the Natural Sciences Engineering Research Council of Canada, the Canadian Institutes of Health Research and the Max Stern Fellowship of McGill University. The authors would like to dedicate this work in loving memory of Margherita B. Rapagna (25 August, 1968–20 May, 2002). Notes ‡ Present address: McConnell Brain Imaging Center, Montréal Neurological Institute, 3801 University Street, Montréal, Québec H3A 2B4, Canada. Additional informationNotes on contributorsRobert E. Kearney ‡ Present address: McConnell Brain Imaging Center, Montréal Neurological Institute, 3801 University Street, Montréal, Québec H3A 2B4, Canada.
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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