Bibliographic record
Abstract
The use of local likelihood methods (Tibshirani and Hastie 1987; Loader 1999) in the presence of data that are either interval or area censored leads naturally to the consideration of EM-type strategies, or rather local-EM algorithms. In this article we consider a class of local-EM algorithms suitable for density or intensity estimation in the temporal or spatial context. We demonstrate that using a piecewise constant density function at the E-step results in the algorithm collapsing explicitly into an EMS algorithm of the type considered by Silverman et al. (1990). This discovery has two advantages. Identifying a relationship between local likelihood and the EMS algorithm means the former provides a natural context for the latter further to that given by Nychka (1990). In addition, the latter guides the implementation, and interpretation, for local-EM algorithms. For example, we expose a previously unknown connection between local-EM algorithms and penalized likelihood that is similar to the more familiar pairing of EM and likelihood. Examples include exploring the spatial structure of the disease Lupus in the City of Toronto. Supplemental materials for this article are available online.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".