EM algorithm for the destructive generalized power series cure model incorporating dependence
Bibliographic record
Abstract
In this thesis, we consider a competing cause scenario that incorporates a dependence structure. The correlated dependence generalized power series (CDGPS) cure rate model, used by Borges et al. (2012), is employed which captures the real life mechanisms that exist for dependent competing causes. Our approach assumes that the number of initial competing causes follows the generalized power series with a destructive process following an administered treatment. This destructive process allows us to capture the undamaged portion of the initial competing causes in a competitive scenario. The objective is to use the estimation-maximization (EM) algorithm on special cases of the CDGPS model to test its effectiveness at estimating model parameters, through a simulation study carried out under various parameter settings. Finally, the EM algorithm and the models are applied to two real melanoma data sets, where model discrimination is conducted through information-based methods.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".