A unified Bayesian approach for modeling zero-inflated count and continuous outcomes
Bibliographic record
Abstract
This article reexamines zero-inflated count and semi-continuous models for analyzing data exhibiting an excess of zeros. Most of these models seem to share a common structure belonging to the exponential dispersion family (EDF) of distributions and the two-part hurdle model. When examining cross-sectional outcomes with a distribution belonging to the EDF, several hurdle models have been explored. This includes recently utilized models as well as some new models that are described in detail here. Then, a unified Markov Chain Monte Carlo (MCMC) method is presented for analyzing data with outcomes belonging to the EDF. Furthermore, a user-friendly R package called UHM (unified hurdle models) has been developed and made available on the Comprehensive R Archive Network (CRAN). This package enables users to easily obtain Bayesian estimates of parameters of interest for hurdle models. Finally, the methods developed in this study are applied to analyze two real datasets featuring count and continuous outcomes with a high prevalence of zero values. Additionally, simulation studies are performed to demonstrate and assess the performance of the proposed models.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".