An ANOVA Test for Parameter Estimability using Data Cloning with\n Application to Statistical Inference for Dynamic Systems
Bibliographic record
Abstract
Models for complex systems are often built with more parameters than can be\nuniquely identified by available data. Because of the variety of causes,\nidentifying a lack of parameter identifiability typically requires mathematical\nmanipulation of models, monte carlo simulations, and examination of the Fisher\nInformation Matrix. A simple test for parameter estimability is introduced,\nusing Data Cloning, a Markov Chain Monte Carlo based algorithm. Together, Data\ncloning and the ANOVA based test determine if the model parameters are\nestimable and if so, determine their maximum likelihood estimates and provide\nasymptotic standard errors. When not all model parameters are estimable, the\nData Cloning results and the ANOVA test can be used to determine estimable\nparameter combinations or infer identifiability problems in the model\nstructure. The method is illustrated using three different real data systems\nthat are known to be difficult to analyze.\n
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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 | 0.022 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".