A Comparison of Information Criterion for Choosing Copula Models
Bibliographic record
Abstract
The object of this paper is to analyse the ability of the Information Criterion in selecting the best copula model. For this study, we carry out a simulation exercise considering five one-parameter copula families: Normal, Student-t with ν degree freedom, Clayton, Gumbel and Frank. For each family copulas, three degrees of dependence and three size samples. The Information Criterion included in the comparison are AIC, BIC, HQIC, SIC. The results obtained are as follow; (i) we find that for a high dependence level (0.9) the reliability of the Information Criterion (IC) is quite good, but it reduces with the dependence level; (ii) the performance of the IC not only depends on the dependence degree but the size sample. In the case of considering negative dependence the reliability of the IC does not depend on the dependence degree but the size sample. As the size sample reduce the performed of the IC reduce. To last, in a comparison among the IC considered, we find that the BIC criterion is the most reliable follow by SIC. AIC and HQIC reaps similar results.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".