Bibliographic record
Abstract
Summary The Advanced Measurement Approach (AMA) to operational risk, as described by the Basel Committee on Banking Supervision ( ), provides a framework meant to be used by banks for establishing the capital required to be set aside to cover worst‐case operational loss scenarios. The problems raised by an AMA approach are primarily statistical in nature, and many lie at the frontier of statistical research. The aim of this paper is to contribute to one of the more pressing challenges of an AMA, namely that of testing the goodness of fit (GoF) of a distributional family to operational loss data. Our focus is on extending certain classically known tests, such as that of Anderson–Darling, with particular emphasis on the right tails of the distributions. The nature of such GoF tests is examined in detail, and computational efficiency of the procedures is taken into account. We also propose a novel saddlepoint approximation method for assessing the asymptotic null distributions of the test statistics based on the eigenvalues of covariance kernels estimated via a jackknife and influence function‐based approach.
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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 | 0.037 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".