Pseudo-likelihood ratio tests for semiparametric multivariate copula model selection
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Bibliographic record
Abstract
The authors propose pseudo-likelihood ratio tests for selecting semiparametric multivariate copula models in which the marginal distributions are unspecified, but the copula function is parameterized and can be misspecified. For the comparison of two models, the tests differ depending on whether the two copulas are generalized nonnested or generalized nested. For more than two models, the procedure is built on the reality check test of White (2000). Unlike White (2000), however, the test statistic is automatically standardized for generalized nonnested models (with the benchmark) and ignores generalized nested models asymptotically. The authors illustrate their approach with American insurance claim data. Tests du rapport des pseudo-vraisemblances pour la sélection de modèles de copules multivariés semiparamétriques: Les auteurs proposent l'emploi de tests du rapport des pseudo-vraisemblances pour la sélection de modéles de copules multivariés semiparamétriques dans lesquels les marges ne sont pas précisées et la copule paramétrique peut éventuellement ětre ma1 spécifiée. La forme du test permettant de comparer deux modèles varie selon que les copules sous-jacentes sont emboitées ou non dans un sens large. La procédure permettant de comparer plusieurs modèles à la fois s'inspire du test de réalisme de White (2000). À la différence de ce demier, cependant, la statistique du test est automatiquement standardisée (par rapport à un étalon) pour les modèles non-emboités et fait fi, asymptotiquement, des modèles emboîtés. Les auteurs illustrent leur approche à l'aide de donntes américaines de sinistres en assurance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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