Estimation for Zero-Inflated Beta-Binomial Regression Model with Missing Response and Covariate Measurement Error
Bibliographic record
Abstract
Discrete, binary data with over-dispersion and zero-inflation can arise in toxicology and other similar fields. In studies where the litter is an experimental unit, there is a ``litter effect" which means that the litter mates respond more alike than animals from other litters. In experimental data, foetuses in the same litter have similar responses to the treatment. The probability of ``success" may not remain constant throughout the litters. In regression analysis of such data another problem that may arise in practice is that some responses may be missing or/and some covariates may have measurement error. In this dissertation we develop an estimation procedure for the parameters of a zero-inflated over-dispersed binomial model in the presence of missing responses without/with considering covariate measurement errors. A weighted expectation maximization algorithm is used for the maximum likelihood (ML) estimation of the parameters involved. Extensive simulations are conducted to study the properties of the estimates in terms of average estimates (AE), relative bias (RB), variance (VAR), mean squared error (MSE) and coverage probability (CP) of estimates. Simulations show much superior properties of the estimates obtained using the weighted expectation maximization algorithm. Some illustrative examples and a discussion are given.
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.
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 | 0.045 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".