Bias correction of two‐state latent Markov process parameter estimates under misclassification
Why this work is in the frame
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Bibliographic record
Abstract
A discretely observed two-state process may misclassify the state of an unobservable continuous-time, two-state Markov process. We examine the behaviour of maximum likelihood transition probability estimates as functions of known misclassification probabilities. Since maximum likelihood estimators are not available in closed form, we provide two alternatives for bias-adjusted estimation. In the case of large samples, the asymptotic bias is quantified and estimators are constructed iteratively using transition counts and specified misclassification probabilities. For finite samples, we provide an approximation based on partial derivatives. Estimators that are bias-adjusted to a first approximation are easily constructed and may serve well when misclassification probabilities are known to be small. Simulation studies reveal the effect of misclassification on estimation. Repeated diagnostic testing data illustrate the approaches.
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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 | 0.002 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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