Extremes, Extreme Spacings and Tail Lengths : An Investigation for Some Important Distributions
Bibliographic record
Abstract
Extreme value and extreme spacing distributions are elegant and important artifacts of statistical theory and practice. However, in statistical education, due to the highly technical nature of the subject, they are generally treated as special topics. But, as demonstrated by Freimer et al. (1989), the asymptotic distributions of the extremes and extreme spacings of random samples, and the related theory can be derived and developed by applying elementary methods to the population quantile functions when they are available in closed forms. However, their work excluded the pedagogically important Gaussian and gamma populations. In this paper, using the closed form expression for the quantile function of the Pareto family first, we show how this approach works. We then proceed to demonstrate its use by simple Taylor expansion for the normal and gamma populations, cases where the closed form expressions for the quantile functions are unavailable. In the process, we relate the geometric notion of tail length to the extreme value distribution. We also examine the case of inverse Gaussian (IG) family, which is well known to be strikingly and intriguingly analogous to the Gaussian family. Actually, in the present context we consider not only the IG but the related reciprocal IG (RIG) and the root reciprocal IG (RRIG) families. The extreme value theory for these three families is derived, again using the elementary methods, even though their quantile functions also lack closed form expressions. Interestingly, it is seen that the extreme value theory for the RRIG population, and not of the IG population, is somewhat analogous to the Gaussian distribution. AMS (2000) Subject Classification : 62E20.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".