Bayesian inference for zero-inflated negative binomial lindley model of overdispersed count data with excess zeros
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Bibliographic record
Abstract
This article aims to develop the zero-inflated negative binomial-Lindley regression model to address the complexity of count data with zero excess and over-dispersion. The proposed compound distribution combines the zero generation mechanism with the Lindley distribution process, and the Bayesian hierarchical framework with MCMC sampling is adopted for parameter estimation, overcoming the limitations of traditional count models in handling complex data structures. The model is applied to two real datasets, one of which is characterized by a large number of zero observations. Its performance is compared with that of the NB-L and NB model. The results show that when the dataset presents the large number of zero values and the long tail feature, the ZINB-L GLM describes the dataset better than the other models.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 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