Alpha Power Type II-G Family: Adding a Power Parameter of Distributions
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
This paper introduces a new family of distributions named the Alpha Power Type II-G (APII-G) family, which emerges as a groundbreaking modeling strategy for examining data governed by univariate continuous distributions.This family aims to enhance the modeling capabilities of continuous prior distributions to better fit the data utilizing a new function encompassing the additional parameter power.The innovative methodology implemented encompasses two continuous distributions: firstly, the oneparameter exponential distribution, which engendered a fresh two-parameter, Alpha Power II Exponential (APIIE) distribution, and secondly, the two-parameter Weibull distribution, which yielded a new three-parameter, Alpha Power II Weibull (APIIW) distribution.Moreover, a scrutiny of the characteristics and statistical functions, and the estimations of the parameters of the two distributions.The efficacy of these estimators is substantiated through simulation studies and finding the mean square error (MSE) and bias values of the estimators compared to sample sizes.It has been empirically proven that the two suggested models outperformed the asymptotic distributions they were compared against using multiple goodness-fit criteria as Akaike information criterion (AIC), Bayesian information criterion (BIC), corrected AIC (CAIC) and Hannan-Quinn information criterion (HQIC) on authentic datasets, The values of these criteria appeared to be the lowest for the two new distributions, which means that the new distributions are the best, especially in the context of the given data.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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