Bibliographic record
Abstract
Composite likelihood is a particular pseudo-likelihood built by adequately combining likelihoods based on lower dimensional events. It appears to be a very appealing alternative to the standard likelihood when the latter is too time-consuming to evaluate or unavailable due to a complex, and possibly unknown, structure of dependence in the data. After the brief ntroduction in the first chapter, Chapter 2 gives notation and basic definitions, but also states a condition for full efficiency of the maximum composite likelihood estimator in exponential families. The core of the thesis is Chapter 3, where we explore a linear combination of two types of composite likelihood which leads to a new objective function that depends on a constant to be chosen. In particular, this new combined composite likelihood uses both bivariate margins and univariate margins. Exact and asymptotic properties are explored. The exact properties lead to the identification of a possible strategy for finding the range of admissible values for the constant. The resulting estimator enjoys desirable asymptotic properties such as consistency and asymptotic normality. Two examples are analyzed in details, also through simulation studies. Chapter 4 studies a weighted independence likelihood in a prediction framework. The aim of this chapter is to determine the weights in order to get an improved prediction of a component of interest of the data vector. In particular, the weights are calculated by means of a delete-one approach in a cross-validation procedure. Through simulation studies, situations in which the weighted independence likelihood works well with respect to the standard independence likelihood are highlighted
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".