Using Artificial Censoring to Improve Extreme Tail Quantile Estimates
Bibliographic record
Abstract
Summary Under certain regularity conditions, maximum-likelihood-based inference enjoys several optimality properties, including high asymptotic efficiency. However, if the distribution of the data deviates slightly from the model proposed, the statistical properties of inference methods based on maximum likelihood can quickly deteriorate. We focus on the situation when the interest lies in one of the tails of the distribution, e.g. when we are estimating a high or low quantile. In this case, it may be natural, if slightly unorthodox, to consider models that fit well the corresponding tail of the sample, rather than its whole range. For example, if we are interested in estimating the fifth percentile, we can pretend that all observations above the 10th percentile have been censored and fit a parametric censored model to the lower tail of the sample. Such an approach, which we call ‘artificial censoring’, has been studied in the engineering literature. We study a data-dependent method to select the amount of artificial censoring and show that it compares favourably with the optimally chosen (‘oracle’) method, which is generally unavailable in practice. We also show that the artificial censoring approach can be applied to estimate tail dependence parameters in copula models, and that it performs well both in simulation and in real data studies.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".