A local moment type estimator for the extreme value index in regression with random covariates
Bibliographic record
Abstract
Abstract This article deals with the nonparametric estimation of the conditional extreme value index of a response in the presence of random covariates. In particular, it is assumed that the conditional response distribution belongs to the max‐domain of attraction of the extreme value distribution, and its index is estimated locally within a narrow neighbourhood of the point of interest in the covariate space. The moment estimator, originally introduced in Dekkers, Einmahl, & de Haan (1989), is adjusted to the local estimation context, and its asymptotic properties are investigated under some mild conditions on the response distribution, the density function of the covariates, the kernel function and for appropriately chosen sequences of bandwidth and threshold parameters. The finite sample performance of the proposed estimator is evaluated by means of an extensive simulation study where a comparison with alternatives from the recent literature is provided. We also illustrate the practical applicability of the estimator on the world catalogue of earthquake magnitudes. The Canadian Journal of Statistics 42: 487–507; 2014 © 2014 Statistical Society of Canada
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".