Bibliographic record
Abstract
AbstractIn this note, we give an explicit expression for the quantile of a mixture of tworandom variables. We carefully examine all possible cases of discrete and continuousvariables with possibly unbounded support. The result is useful for finding boundson the Value-at-Risk of risky portfolios when only partial information is available(Bernard and Vanduffel (2014)). ∗ Carole Bernard, Department of Statistics and Actuarial Science at the University of Waterloo (email:c3bernar@uwaterloo.ca). † Corresponding author : Steven Vanduffel, Department of Economics and Political Sciences at VrijeUniversiteit Brussel (VUB). (e-mail: steven.vanduffel@vub.ac.be). ‡ C. Bernard gratefully acknowledges support from the Natural Sciences and Engineering ResearchCouncil of Canada, the Humboldt Research Foundation and the hospitality of the chair of mathematicalstatistics of Technische Universit¨at Mu¨nchen where the paper was completed. S. Vanduffel acknowledgesthe financial support of the BNP Paribas Fortis Chair in Banking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".