Modelling the proportions with excessive endpoints based on a generalized Lindley binomial model
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
<title>Abstract</title> This paper presents the generalized Lindley binomial (GLB) distribution, a novel probability distribution designed for the analysis of proportional data with excessive endpoints. The study delves into the probabilistic characteristics of the GLB distribution, including the probability mass function and the rth factorial moment function. Estimation of the distribution parameters in the GLB model, both with and without covariates, is addressed using the Fisher scoring algorithm and the EM algorithm. Furthermore, the paper explores techniques for model diagnosis and evaluates the goodness of fit for the proposed GLB model. To illustrate the performance of the derived EM algorithms in parameter estimation, a limited simulation study is conducted for both cases, with and without covariates, in the GLB model. The practical application of the proposed Lindley binomial regression model is demonstrated using the whitefly dataset.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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