Local likelihood density estimation for interval censored data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The authors propose a class of procedures for local likelihood estimation from data that are either interval-censored or that have been aggregated into bins. One such procedure relies on an algorithm that generalizes existing self-consistency algorithms by introducing kernel smoothing at each step of the iteration. The entire class of procedures yields estimates that are obtained as solutions of fixed point equations. By discretizing and applying numerical integration, the authors use fixed point theory to study convergence of algorithms for the class. Rapid convergence is effected by the implementation of a local EM algorithm as a global Newton iteration. The latter requires an explicit solution of the local likelihood equations which can be found by using the symbolic Newton-Raphson algorithm, if necessary. Estimation de la densité par vraisemblance locale à partir de données censurées par intervalle: Les auteurs proposent une classe de procédures pour l'estimation de la densité par vraisemblance locale lorsque les données sont censurées par intervalle ou qu'elles ont été regroupées en classes. L'une de ces procédures s'appuie sur un algorithme qui, en faisant appel à un noyau lissant à chaque itération, généralise les algorithmes auto-convergents déjà existants. Les estimations auxquelles la classe conduit sont des points fixes de certaines équations. En s'appuyant sur des techniques de discrétisation et d'intégration numérique, les auteurs se servent de la théorie des points fixes pour étudier la convergence des algorithmes de la classe. La convergence est accélérée par l'emploi d'un algorithme EM local dans l'itération globale de la méthode de Newton. Cette demière fait intervenir une solution d'équations de vraisemblance locale qui, au besoin, peut être trouvée au moyen d'un algorithme de Newton-Raphson symbolique.
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.
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 | 0.001 | 0.008 |
| 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 it