Local Likelihood Density Estimation and Value at Risk
Bibliographic record
Abstract
Local Likelihood Density Estimation, and Value at Risk. In this paper we fit locally a parametric model to observations lying in a neighborhood of a predetermined value c. This approach provides an instrument of tail analysis, called the local parameter function, which represents the dependence of the estimated parameters on c. As well, a new local likelihood density estimator is proposed. The method is applicable to risk analysis, and especially to the computation of the conditional VaR. An empirical example is presented in the paper. Keywords: Local Likelihood, Kernel, Localized Values at Risk. JEL : C14, C32 THIS VERSION: March 13, 2000 0 R'esum'e Estimation de densit'e par vraisemblance locale et Valeur `a Risque Dans ce papier nous ajustons localement un mod`ele param'etrique aux observations se trouvant dans un voisinage d'une valeur pr'edetermin'ee c. Cette approche fournit un outil d'analyse des queues de distribution, appel'ee fonction de param`etre locale, qui capture la d...
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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 | 0.009 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".