Multivariate Generalized Linear Mixed Models with High Complexity / Modèles linéaires généralisés mixtes multivariés avec complexité élevée
Bibliographic record
Abstract
The theory of exponential dispersion models (EDM), for which Bent Jørgensen made substantial contributions, provides a flexible framework of models alternative to the classic Gaussian linear models (e.g. generalized linear models and additive models). I review some multivariate extensions of those models that allow the distribution of the different dimensions to belong to different EDMs. As an illustration, I present some applications in quantitative genetics with high complexity (several hundreds of thousand observations and deep pedigrees). In all the presented applications, it is crucial to understand the underlying stochastic process related to the EDMs used to represent well and interpret biological questions of interest. Bent Jørgensen advocated similar ideas in his work since the 1980s. La théorie des modèles de dispersion exponentielle (EDM), à laquelle Bent Jørgensen a apporté d'importantes contributions, fournit un cadre flexible de modèles alternatifs aux modèles linéaires gaussiens classiques (par exemple, les modèles linéaires et les modèles additifs). J'examinerai quelques extensions multivariées de ces modèles qui permettent à la loi des différentes dimensions d'appartenir à différents EDM. À titre d'exemple, je présenterai quelques applications à la génétique quantitative avec complexité élevée (plusieurs centaines de milliers d'observations et des pedigrees profonds). Dans toutes les applications présentées, il est essentiel de comprendre le processus stochastique sous-jacent associé aux EDMs pour bien représenter et interpréter les questions biologiques d'intérêt. Bent Jørgensen prônait des idées similaires dans son travail depuis les années 1980.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".