Parametric estimation for the Birnbaum-saunders Lifetime Distribution based on a new parametrization
Bibliographic record
Abstract
In this article, we consider a new parametrization of the two-parameter Birnbaum-Saunders lifetime distribution. Importantly, this re-parametrization fits the physics of studying phenomena since the proposed parameters characterize or specify the thickness of the sample and the nominal treatment loading on the sample, respectively. The usual shape and scale parameters of the distribution do not offer this physical interpretation. Instead of substitution method of the parameter estimators of the original Birnbaum-Saunders model into the new model, the statistical properties of the direct application of the standard methods of point estimation to the new parameters are investigated. In an effort to appraise the performance of proposed estimators in a practical setting, Monte-Carlo simulations are conducted for small, moderate and large sample sizes. Two real life examples based on published data are used to illustrate the suggested estimation methods. Some concluding remarks and areas for further research are also presented.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".