Birnbaum–Saunders Distribution Based on Asymmetric Heavy-Tailed Distributions, Associated Inference, and Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Birnbaum–Saunders (BS) distribution has received considerable attention in the statistical literature, both in applied and theoretical problems. Even though much work has been done on extensions of the BS distribution, there is still a need for models for predicting extreme percentiles and for fitting data that are highly concentrated on the left-tail of the distribution. This article proposes a robust extension of the BS distribution, based on scale mixtures of skew-normal distributions that can be used to model highly asymmetric data. This extension provides flexible heavy-tailed distributions which can be used in the robust estimation of parameters in the presence of outlying observations, as well as an EM-algorithm for the maximum likelihood estimation of model parameters. Finally, the proposed model and methods of inference are examined and illustrated by means of Monte Carlo simulation studies and a real data set.
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.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it